From b6810bc4db95cffef62d10616db368ef91b5e8c0 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Mon, 22 Sep 2025 10:14:32 -0700 Subject: [PATCH 01/19] add trackio --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 174 ++++++++++++++++------------- 1 file changed, 94 insertions(+), 80 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 35598701..ff79f9c8 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -10,7 +10,7 @@ "
\n", "\n", "\n", - " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", @@ -38,7 +38,7 @@ "\n", "Introducing Unsloth [Standby for RL](https://docs.unsloth.ai/basics/memory-efficient-rl): GRPO is now faster, uses 30% less memory with 2x longer context.\n", "\n", - "Gpt-oss fine-tuning now supports 8\u00d7 longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training)\n", + "Gpt-oss fine-tuning now supports 8× longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training)\n", "\n", "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", "\n", @@ -61,7 +61,21 @@ "id": "dqkFWxkVnVgc" }, "outputs": [], - "source": "%%capture\n# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2" + "source": [ + "%%capture\n", + "# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n", + "!pip install --upgrade -qqq uv\n", + "try: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\n", + "except: get_numpy = \"numpy\"\n", + "!uv pip install -qqq \\\n", + " \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n", + " \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n", + " \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n", + " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", + "!uv pip install transformers==4.55.4\n", + "!uv pip install --no-deps trl==0.22.2\n", + "!uv pip install trackio<1.0" + ] }, { "cell_type": "markdown", @@ -220,8 +234,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n", + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2025.8.5: Fast Gpt_Oss patching. Transformers: 4.56.0.dev0.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.8.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.4.0\n", @@ -848,18 +862,18 @@ "\n", "reasoning language: French\n", "\n", - "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus r\u00e9centes. Tout d'abord, je dois v\u00e9rifier si j'ai acc\u00e8s \u00e0 des donn\u00e9es en temps r\u00e9el. \u00c9tant donn\u00e9 que je ne peux pas naviguer sur Internet ou acc\u00e9der directement \u00e0 l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils g\u00e9n\u00e9raux sur la fa\u00e7on de les trouver.\n", + "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.\n", "\n", - "Je devrais pr\u00e9ciser que les tendances Twitter \u00e9voluent rapidement et sont sp\u00e9cifiques \u00e0 chaque r\u00e9gion. Je pourrais sugg\u00e9rer de consulter la section \u00ab\u00a0En vogue\u00a0\u00bb sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient \u00eatre utiles. Il est important de souligner que les tendances varient selon la r\u00e9gion et l'heure de la journ\u00e9e. Je devrais garder un ton amical et bienveillant, peut-\u00eatre ajouter un emoji pour rester l\u00e9ger. Je vais structurer ma r\u00e9ponse \u00e9tape par \u00e9tape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des donn\u00e9es en temps r\u00e9el et proposer d'autres m\u00e9thodes. Je conserverai un langage simple et convivial, en \u00e9vitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", + "Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", "\n", - "1. **Open the \"Trending\" tab** on the Twitter app or website \u2013 it updates constantly! \n", - "2. **Search for hashtags** like #Trending or #Viral to see what\u2019s blowing up. \n", + "1. **Open the \"Trending\" tab** on the Twitter app or website – it updates constantly! \n", + "2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up. \n", "3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads). \n", - "4. **Check regional trends** \u2013 they often differ by location! \n", + "4. **Check regional trends** – they often differ by location! \n", "\n", "Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the \"Distracted Boyfriend\" meme revival), celebrity drama, or unexpected events (hello, weather disasters!). \n", "\n", - "Want me to brainstorm *what* might trend next? I\u2019ve got ideas!<|return|>\n" + "Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>\n" ] } ], @@ -889,7 +903,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -953,7 +967,7 @@ " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", - " report_to = \"none\", # Use this for WandB etc\n", + " report_to = \"trackio\",\n", " ),\n", ")" ] @@ -1386,7 +1400,7 @@ "\n", "reasoning language: French\n", "\n", - "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 \u2192 x^5 + 3x^\n" + "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 → x^5 + 3x^\n" ] } ], @@ -1461,7 +1475,7 @@ " \n", " \n", "\n", - " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "\n" ] } @@ -1789,9 +1803,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_83dd0a7d75d544f1a64fb265822b1dc6", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_28b1a6aef393405ba325d29e470b9332", - "value": "\u200722.8k/?\u2007[00:00<00:00,\u20071.88MB/s]" + "value": " 22.8k/? [00:00<00:00, 1.88MB/s]" } }, "0fc33d9d7b2e486ea16c7e9655d1f078": { @@ -1992,9 +2006,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_98122f1f5c974405aec8cee21d511235", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_60ee8e94b3794c6085a03a96058d03ee", - "value": "\u20074.00G/4.00G\u2007[00:47<00:00,\u2007171MB/s]" + "value": " 4.00G/4.00G [00:47<00:00, 171MB/s]" } }, "19983e4ce30944c7a57abfe01e463eb0": { @@ -2013,9 +2027,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9643968ed03642429372c2dac797031b", - 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"placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_3c88be2e8d5b4559b7c1928e7a46e847", - "value": "\u200715.1k/?\u2007[00:00<00:00,\u2007901kB/s]" + "value": " 15.1k/? [00:00<00:00, 901kB/s]" } }, "7e5c3cad61f9447dbfdc25e3487223b7": { @@ -4548,9 +4562,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_040250e6afb74feeb107c69e50a985bc", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_5d6c9f818ec94c5d9f8b325839371963", - "value": "\u200727.9M/27.9M\u2007[00:00<00:00,\u200742.9MB/s]" + "value": " 27.9M/27.9M [00:00<00:00, 42.9MB/s]" } }, "8d0635071af84cf1ac18e9a052087e32": { @@ -4636,9 +4650,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_297f17e5d1e743c7acea1d15731d255e", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_30893988a2a4460696d92911a4ebede7", - "value": "\u20075.29M/5.29M\u2007[00:00<00:00,\u20078.80MB/s]" + "value": " 5.29M/5.29M [00:00<00:00, 8.80MB/s]" } }, "8ec51fbe49f74f82b0f13c658f5d6bf8": { @@ -4930,9 +4944,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8f39efb61c224ae18db657ce38efd085", - 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"placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_f3c6916566f0483082b75a6232501001", - "value": "model-00002-of-00004.safetensors:\u2007100%" + "value": "model-00002-of-00004.safetensors: 100%" } }, "d9b1cfdaa58f4a579addc1bfb41e3622": { @@ -6364,9 +6378,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8d925b65a79240f0bad9cd8add2bfec7", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_cb7de23470ce4dbbbb3a636d1aa0af9c", - "value": "\u20074/4\u2007[01:00<00:00,\u200712.86s/it]" + "value": " 4/4 [01:00<00:00, 12.86s/it]" } }, "ded74fd1bf114fe1a7c3d1bc0b6dd6ab": { @@ -6489,9 +6503,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_11ada4258a894a27a4e096257ecac8ff", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_f527df8dc8734cbcac2bfe27faaa7dfa", - "value": "chat_template.jinja:\u2007" + "value": "chat_template.jinja: " } }, "e75b2c318d464bb8b4debc68621cb533": { @@ -6510,9 +6524,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e3a9a9b8868e40c3b754b4fb6a299906", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_29f0d621132742188596ce3a7dfb1704", - "value": "data/train-00000-of-00001.parquet:\u2007100%" + "value": "data/train-00000-of-00001.parquet: 100%" } }, "ebb49ff5feff47aca6953a77806bfcc0": { @@ -6583,9 +6597,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f14e045ddcf54eef958e92c7a8616d50", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_8d0635071af84cf1ac18e9a052087e32", - "value": "README.md:\u2007" + "value": "README.md: " } }, "ecb9b5a306cc4244a12f8bdd7c65e498": { @@ -6924,9 +6938,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_04bc14d9112242259867abad6efc53c3", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_2e9287b93e93412b9f2b12cd98d69ab6", - "value": "\u20071000/1000\u2007[00:00<00:00,\u20071151.76\u2007examples/s]" + "value": " 1000/1000 [00:00<00:00, 1151.76 examples/s]" } }, "ff74e51179ab471b898e11008c91629e": { @@ -6959,4 +6973,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 35e665cc0a9a5ec86f63bddf835fd84c71f97c08 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Mon, 22 Sep 2025 10:28:05 -0700 Subject: [PATCH 02/19] quotes --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index ff79f9c8..a951aa5c 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -74,7 +74,7 @@ " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", "!uv pip install transformers==4.55.4\n", "!uv pip install --no-deps trl==0.22.2\n", - "!uv pip install trackio<1.0" + "!uv pip install \"trackio<1.0\"" ] }, { From 9fd98169681f4fde84abca4d0bc3ae9d67dce52d Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Mon, 22 Sep 2025 10:29:38 -0700 Subject: [PATCH 03/19] revert --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 174 +++++++++++++---------------- 1 file changed, 80 insertions(+), 94 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index a951aa5c..35598701 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -10,7 +10,7 @@ "
\n", "\n", "\n", - " Join Discord if you need help + ⭐ Star us on Github ⭐\n", + " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", @@ -38,7 +38,7 @@ "\n", "Introducing Unsloth [Standby for RL](https://docs.unsloth.ai/basics/memory-efficient-rl): GRPO is now faster, uses 30% less memory with 2x longer context.\n", "\n", - "Gpt-oss fine-tuning now supports 8× longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training)\n", + "Gpt-oss fine-tuning now supports 8\u00d7 longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training)\n", "\n", "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", "\n", @@ -61,21 +61,7 @@ "id": "dqkFWxkVnVgc" }, "outputs": [], - "source": [ - "%%capture\n", - "# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n", - "!pip install --upgrade -qqq uv\n", - "try: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\n", - "except: get_numpy = \"numpy\"\n", - "!uv pip install -qqq \\\n", - " \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n", - " \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n", - " \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n", - " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", - "!uv pip install transformers==4.55.4\n", - "!uv pip install --no-deps trl==0.22.2\n", - "!uv pip install \"trackio<1.0\"" - ] + "source": "%%capture\n# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2" }, { "cell_type": "markdown", @@ -234,8 +220,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "🦥 Unsloth Zoo will now patch everything to make training faster!\n", + "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2025.8.5: Fast Gpt_Oss patching. Transformers: 4.56.0.dev0.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.8.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.4.0\n", @@ -862,18 +848,18 @@ "\n", "reasoning language: French\n", "\n", - "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.\n", + "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus r\u00e9centes. Tout d'abord, je dois v\u00e9rifier si j'ai acc\u00e8s \u00e0 des donn\u00e9es en temps r\u00e9el. \u00c9tant donn\u00e9 que je ne peux pas naviguer sur Internet ou acc\u00e9der directement \u00e0 l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils g\u00e9n\u00e9raux sur la fa\u00e7on de les trouver.\n", "\n", - "Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", + "Je devrais pr\u00e9ciser que les tendances Twitter \u00e9voluent rapidement et sont sp\u00e9cifiques \u00e0 chaque r\u00e9gion. Je pourrais sugg\u00e9rer de consulter la section \u00ab\u00a0En vogue\u00a0\u00bb sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient \u00eatre utiles. Il est important de souligner que les tendances varient selon la r\u00e9gion et l'heure de la journ\u00e9e. Je devrais garder un ton amical et bienveillant, peut-\u00eatre ajouter un emoji pour rester l\u00e9ger. Je vais structurer ma r\u00e9ponse \u00e9tape par \u00e9tape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des donn\u00e9es en temps r\u00e9el et proposer d'autres m\u00e9thodes. Je conserverai un langage simple et convivial, en \u00e9vitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", "\n", - "1. **Open the \"Trending\" tab** on the Twitter app or website – it updates constantly! \n", - "2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up. \n", + "1. **Open the \"Trending\" tab** on the Twitter app or website \u2013 it updates constantly! \n", + "2. **Search for hashtags** like #Trending or #Viral to see what\u2019s blowing up. \n", "3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads). \n", - "4. **Check regional trends** – they often differ by location! \n", + "4. **Check regional trends** \u2013 they often differ by location! \n", "\n", "Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the \"Distracted Boyfriend\" meme revival), celebrity drama, or unexpected events (hello, weather disasters!). \n", "\n", - "Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>\n" + "Want me to brainstorm *what* might trend next? I\u2019ve got ideas!<|return|>\n" ] } ], @@ -903,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -967,7 +953,7 @@ " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", - " report_to = \"trackio\",\n", + " report_to = \"none\", # Use this for WandB etc\n", " ),\n", ")" ] @@ -1400,7 +1386,7 @@ "\n", "reasoning language: French\n", "\n", - "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 → x^5 + 3x^\n" + "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 \u2192 x^5 + 3x^\n" ] } ], @@ -1475,7 +1461,7 @@ " \n", " \n", "\n", - " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", + " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", "\n" ] } @@ -1803,9 +1789,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_83dd0a7d75d544f1a64fb265822b1dc6", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_28b1a6aef393405ba325d29e470b9332", - "value": " 22.8k/? [00:00<00:00, 1.88MB/s]" + "value": "\u200722.8k/?\u2007[00:00<00:00,\u20071.88MB/s]" } }, "0fc33d9d7b2e486ea16c7e9655d1f078": { @@ -2006,9 +1992,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_98122f1f5c974405aec8cee21d511235", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_60ee8e94b3794c6085a03a96058d03ee", - "value": " 4.00G/4.00G [00:47<00:00, 171MB/s]" + "value": "\u20074.00G/4.00G\u2007[00:47<00:00,\u2007171MB/s]" } }, "19983e4ce30944c7a57abfe01e463eb0": { @@ -2027,9 +2013,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9643968ed03642429372c2dac797031b", - 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"placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_9518e8ada50747818ad94bf81118a964", - "value": " 1000/1000 [00:00<00:00, 2996.56 examples/s]" + "value": "\u20071000/1000\u2007[00:00<00:00,\u20072996.56\u2007examples/s]" } }, "6378d55aada8467688da8d1da0c123ce": { @@ -3784,9 +3770,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_99dfd860e52240838e9c55238884fcee", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_a17f3673fb6c4971bd53489a80c12b03", - "value": "generation_config.json: 100%" + "value": "generation_config.json:\u2007100%" } }, "65d2db12df6942b98bda16b738191f34": { @@ -3881,9 +3867,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2e560b107cbf4f9ea1b34bf3a3094678", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_74ebde2ac07d49f0ba65b7d70cea09f1", - "value": " 1.16G/1.16G [00:19<00:00, 51.4MB/s]" + "value": "\u20071.16G/1.16G\u2007[00:19<00:00,\u200751.4MB/s]" } }, "69176a4379e74670a765be4b916e718a": { @@ -3902,9 +3888,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5f96703d9fd64ee7b52b02662e7afffc", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_51c530ca4981460c99501f5f90f3a182", - "value": "model-00003-of-00004.safetensors: 100%" + "value": "model-00003-of-00004.safetensors:\u2007100%" } }, "6c279fe5cb444673a65f1caba4648fc4": { @@ -4124,9 +4110,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b372d6ed1c204203be1fac53f2093c62", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_376cd15963c84026a4ba2a2c212b813e", - "value": "model.safetensors.index.json: " + "value": "model.safetensors.index.json:\u2007" } }, "7322a242ad4744168de44963be435725": { @@ -4145,9 +4131,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_88e50815be2a48e2a434b78ea4b98bd2", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_5e42a9d44ffe44eebf95d3bc0fd0f752", - "value": "special_tokens_map.json: 100%" + "value": "special_tokens_map.json:\u2007100%" } }, "737f0b3c8edd40c69ac7025c6ee00723": { @@ -4249,9 +4235,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_6d1644394190402baf9a58b00b1b3de8", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_3c88be2e8d5b4559b7c1928e7a46e847", - "value": " 15.1k/? [00:00<00:00, 901kB/s]" + "value": "\u200715.1k/?\u2007[00:00<00:00,\u2007901kB/s]" } }, "7e5c3cad61f9447dbfdc25e3487223b7": { @@ -4562,9 +4548,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_040250e6afb74feeb107c69e50a985bc", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_5d6c9f818ec94c5d9f8b325839371963", - "value": " 27.9M/27.9M [00:00<00:00, 42.9MB/s]" + "value": "\u200727.9M/27.9M\u2007[00:00<00:00,\u200742.9MB/s]" } }, "8d0635071af84cf1ac18e9a052087e32": { @@ -4650,9 +4636,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_297f17e5d1e743c7acea1d15731d255e", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_30893988a2a4460696d92911a4ebede7", - "value": " 5.29M/5.29M [00:00<00:00, 8.80MB/s]" + "value": "\u20075.29M/5.29M\u2007[00:00<00:00,\u20078.80MB/s]" } }, "8ec51fbe49f74f82b0f13c658f5d6bf8": { @@ -4944,9 +4930,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8f39efb61c224ae18db657ce38efd085", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_2a2612b9d72c49089ebb79bb28c0c415", - "value": " 1.19M/? [00:00<00:00, 60.5MB/s]" + "value": "\u20071.19M/?\u2007[00:00<00:00,\u200760.5MB/s]" } }, "99dfd860e52240838e9c55238884fcee": { @@ -5995,9 +5981,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4362a20e703c42d4b0b92dc410d62889", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_227eab802b6543d8b6915da6fed18c6e", - "value": "Unsloth: Tokenizing ["text"] (num_proc=2): 100%" + "value": "Unsloth:\u2007Tokenizing\u2007["text"]\u2007(num_proc=2):\u2007100%" } }, "c4e07ba599fc462792e39b6f3841ec46": { @@ -6016,9 +6002,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f1237a5c19014663b8ec6475ff81091d", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_585b94dcbd1c4a1595c7c6b110ead7ef", - "value": "model-00001-of-00004.safetensors: 100%" + "value": "model-00001-of-00004.safetensors:\u2007100%" } }, "c5996543c5c346a99000c70e810f8e8c": { @@ -6037,9 +6023,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_bd365bd853fd417aa7b7096ea1e9540c", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_fd15ab7222824c9abcce3a17cc0209af", - "value": "model-00004-of-00004.safetensors: 100%" + "value": "model-00004-of-00004.safetensors:\u2007100%" } }, "cb7de23470ce4dbbbb3a636d1aa0af9c": { @@ -6217,9 +6203,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c0615e2ed6c246d3bd64e50002f1b5cf", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_72986da11c5c400b8f3fcf73cebf8af8", - "value": "tokenizer.json: 100%" + "value": "tokenizer.json:\u2007100%" } }, "d307e2839dae4480b07e25b1db2ff9e1": { @@ -6262,9 +6248,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_add72aaf688a4ad8bfe7b5ffda08d21d", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_70f86aee84a143159feded54e0b0e2ee", - "value": "tokenizer_config.json: " + "value": "tokenizer_config.json:\u2007" } }, "d827c81f690044e2b3002e81be8ccc86": { @@ -6283,9 +6269,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ebb49ff5feff47aca6953a77806bfcc0", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_f3c6916566f0483082b75a6232501001", - "value": "model-00002-of-00004.safetensors: 100%" + "value": "model-00002-of-00004.safetensors:\u2007100%" } }, "d9b1cfdaa58f4a579addc1bfb41e3622": { @@ -6378,9 +6364,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8d925b65a79240f0bad9cd8add2bfec7", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_cb7de23470ce4dbbbb3a636d1aa0af9c", - "value": " 4/4 [01:00<00:00, 12.86s/it]" + "value": "\u20074/4\u2007[01:00<00:00,\u200712.86s/it]" } }, "ded74fd1bf114fe1a7c3d1bc0b6dd6ab": { @@ -6503,9 +6489,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_11ada4258a894a27a4e096257ecac8ff", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_f527df8dc8734cbcac2bfe27faaa7dfa", - "value": "chat_template.jinja: " + "value": "chat_template.jinja:\u2007" } }, "e75b2c318d464bb8b4debc68621cb533": { @@ -6524,9 +6510,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e3a9a9b8868e40c3b754b4fb6a299906", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_29f0d621132742188596ce3a7dfb1704", - "value": "data/train-00000-of-00001.parquet: 100%" + "value": "data/train-00000-of-00001.parquet:\u2007100%" } }, "ebb49ff5feff47aca6953a77806bfcc0": { @@ -6597,9 +6583,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f14e045ddcf54eef958e92c7a8616d50", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_8d0635071af84cf1ac18e9a052087e32", - "value": "README.md: " + "value": "README.md:\u2007" } }, "ecb9b5a306cc4244a12f8bdd7c65e498": { @@ -6938,9 +6924,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_04bc14d9112242259867abad6efc53c3", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_2e9287b93e93412b9f2b12cd98d69ab6", - "value": " 1000/1000 [00:00<00:00, 1151.76 examples/s]" + "value": "\u20071000/1000\u2007[00:00<00:00,\u20071151.76\u2007examples/s]" } }, "ff74e51179ab471b898e11008c91629e": { @@ -6973,4 +6959,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 334cff59c6d672a95dfbf5ae5cf18553f4c3397b Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Mon, 22 Sep 2025 10:33:00 -0700 Subject: [PATCH 04/19] Update gpt-oss-(20B)-Fine-tuning.ipynb --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 35598701..9f9eea59 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -61,7 +61,7 @@ "id": "dqkFWxkVnVgc" }, "outputs": [], - "source": "%%capture\n# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2" + "source": "%%capture\n# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2\n!uv pip install \"trackio<1.0\"" }, { "cell_type": "markdown", @@ -953,7 +953,7 @@ " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", - " report_to = \"none\", # Use this for WandB etc\n", + " report_to = \"trackio\", \n", " ),\n", ")" ] @@ -6959,4 +6959,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 8a72cd0a3fbfd19e4fc8f8f01758c3add72d90b6 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Mon, 13 Oct 2025 18:18:44 -0700 Subject: [PATCH 05/19] changes --- python_scripts/gpt-oss-(20B)-Fine-tuning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python_scripts/gpt-oss-(20B)-Fine-tuning.py b/python_scripts/gpt-oss-(20B)-Fine-tuning.py index 9e2a4e42..eacb1ef0 100644 --- a/python_scripts/gpt-oss-(20B)-Fine-tuning.py +++ b/python_scripts/gpt-oss-(20B)-Fine-tuning.py @@ -74,7 +74,7 @@ model, r = 8, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128 target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", - "gate_proj", "up_proj", "down_proj",], + "gate_proj", "up_proj", "down_proj"], lora_alpha = 16, lora_dropout = 0, # Supports any, but = 0 is optimized bias = "none", # Supports any, but = "none" is optimized From 81984bbe53dcc0e2ef95cedf3d1562bf26b826dc Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Mon, 13 Oct 2025 18:21:03 -0700 Subject: [PATCH 06/19] changes --- python_scripts/gpt-oss-(20B)-Fine-tuning.py | 26 ++++++++++----------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/python_scripts/gpt-oss-(20B)-Fine-tuning.py b/python_scripts/gpt-oss-(20B)-Fine-tuning.py index eacb1ef0..bb3e6cfe 100644 --- a/python_scripts/gpt-oss-(20B)-Fine-tuning.py +++ b/python_scripts/gpt-oss-(20B)-Fine-tuning.py @@ -16,11 +16,11 @@ # ### News # -# Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker). +# [Vision RL](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) is now supported! Train Qwen2.5-VL, Gemma 3 etc. with GSPO or GRPO. # -# [gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels! +# Introducing Unsloth [Standby for RL](https://docs.unsloth.ai/basics/memory-efficient-rl): GRPO is now faster, uses 30% less memory with 2x longer context. # -# Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM. +# Gpt-oss fine-tuning now supports 8× longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training) # # Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning). # @@ -32,7 +32,7 @@ # # In[ ]: # # -# get_ipython().run_cell_magic('capture', '', '!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f"numpy=={numpy.__version__}"\nexcept: get_numpy = "numpy"\n!uv pip install -qqq \\\n "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers>=4.55.3" \\\n "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \\\n "unsloth[base] @ git+https://github.com/unslothai/unsloth" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2\n') +# get_ipython().run_cell_magic('capture', '', '# We\'re installing the latest Torch, Triton, OpenAI\'s Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f"numpy=={numpy.__version__}"\nexcept: get_numpy = "numpy"\n!uv pip install -qqq \\\n "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers>=4.55.3" \\\n "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \\\n "unsloth[base] @ git+https://github.com/unslothai/unsloth" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2\n') # # # # ### Unsloth @@ -74,7 +74,7 @@ model, r = 8, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128 target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", - "gate_proj", "up_proj", "down_proj"], + "gate_proj", "up_proj", "down_proj",], lora_alpha = 16, lora_dropout = 0, # Supports any, but = 0 is optimized bias = "none", # Supports any, but = "none" is optimized @@ -111,7 +111,7 @@ return_tensors = "pt", return_dict = True, reasoning_effort = "low", # **NEW!** Set reasoning effort to low, medium or high -).to("cuda") +).to(model.device) _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -132,7 +132,7 @@ return_tensors = "pt", return_dict = True, reasoning_effort = "medium", # **NEW!** Set reasoning effort to low, medium or high -).to("cuda") +).to(model.device) _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -153,7 +153,7 @@ return_tensors = "pt", return_dict = True, reasoning_effort = "high", # **NEW!** Set reasoning effort to low, medium or high -).to("cuda") +).to(model.device) _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -312,7 +312,7 @@ def formatting_prompts_func(examples): return_tensors = "pt", return_dict = True, reasoning_effort = "medium", -).to("cuda") +).to(model.device) from transformers import TextStreamer _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -354,7 +354,7 @@ def formatting_prompts_func(examples): return_tensors = "pt", return_dict = True, reasoning_effort = "high", -).to("cuda") +).to(model.device) from transformers import TextStreamer _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -368,12 +368,12 @@ def formatting_prompts_func(examples): # Merge and push to hub in mxfp4 4bit format if False: - model.save_pretrained_merged("finetuned_model", tokenizer, save_method = "mxfp4") -if False: model.push_to_hub_merged("repo_id/repo_name", tokenizer, token = "hf...", save_method = "mxfp4") + model.save_pretrained_merged("finetuned_model", tokenizer, save_method="mxfp4") +if False: model.push_to_hub_merged("repo_id/repo_name", tokenizer, token="hf...", save_method="mxfp4") # Merge and push to hub in 16bit if False: - model.save_pretrained_merged("finetuned_model", tokenizer, save_method = "merged_16bit") + model.save_pretrained_merged("finetuned_model", tokenizer, save_method="merged_16bit") if False: # Pushing to HF Hub model.push_to_hub_merged("hf/gpt-oss-finetune", tokenizer, save_method = "merged_16bit", token = "") From 5ec64a6369def5c8d4378c6fd96d54c1bb726adc Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Mon, 13 Oct 2025 18:21:55 -0700 Subject: [PATCH 07/19] revert all changes --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 6 +++--- python_scripts/gpt-oss-(20B)-Fine-tuning.py | 24 ++++++++++----------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 6e94fd2d..acb1d922 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -51,7 +51,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "%%capture\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2\n!uv pip install \"trackio<1.0\"" + "source": "%%capture\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2" }, { "cell_type": "markdown", @@ -943,7 +943,7 @@ " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", - " report_to = \"trackio\", \n", + " report_to = \"none\", # Use this for WandB etc\n", " ),\n", ")" ] @@ -6951,4 +6951,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/python_scripts/gpt-oss-(20B)-Fine-tuning.py b/python_scripts/gpt-oss-(20B)-Fine-tuning.py index bb3e6cfe..9e2a4e42 100644 --- a/python_scripts/gpt-oss-(20B)-Fine-tuning.py +++ b/python_scripts/gpt-oss-(20B)-Fine-tuning.py @@ -16,11 +16,11 @@ # ### News # -# [Vision RL](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) is now supported! Train Qwen2.5-VL, Gemma 3 etc. with GSPO or GRPO. +# Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker). # -# Introducing Unsloth [Standby for RL](https://docs.unsloth.ai/basics/memory-efficient-rl): GRPO is now faster, uses 30% less memory with 2x longer context. +# [gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels! # -# Gpt-oss fine-tuning now supports 8× longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training) +# Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM. # # Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning). # @@ -32,7 +32,7 @@ # # In[ ]: # # -# get_ipython().run_cell_magic('capture', '', '# We\'re installing the latest Torch, Triton, OpenAI\'s Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f"numpy=={numpy.__version__}"\nexcept: get_numpy = "numpy"\n!uv pip install -qqq \\\n "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers>=4.55.3" \\\n "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \\\n "unsloth[base] @ git+https://github.com/unslothai/unsloth" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2\n') +# get_ipython().run_cell_magic('capture', '', '!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f"numpy=={numpy.__version__}"\nexcept: get_numpy = "numpy"\n!uv pip install -qqq \\\n "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers>=4.55.3" \\\n "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \\\n "unsloth[base] @ git+https://github.com/unslothai/unsloth" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2\n') # # # # ### Unsloth @@ -111,7 +111,7 @@ return_tensors = "pt", return_dict = True, reasoning_effort = "low", # **NEW!** Set reasoning effort to low, medium or high -).to(model.device) +).to("cuda") _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -132,7 +132,7 @@ return_tensors = "pt", return_dict = True, reasoning_effort = "medium", # **NEW!** Set reasoning effort to low, medium or high -).to(model.device) +).to("cuda") _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -153,7 +153,7 @@ return_tensors = "pt", return_dict = True, reasoning_effort = "high", # **NEW!** Set reasoning effort to low, medium or high -).to(model.device) +).to("cuda") _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -312,7 +312,7 @@ def formatting_prompts_func(examples): return_tensors = "pt", return_dict = True, reasoning_effort = "medium", -).to(model.device) +).to("cuda") from transformers import TextStreamer _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -354,7 +354,7 @@ def formatting_prompts_func(examples): return_tensors = "pt", return_dict = True, reasoning_effort = "high", -).to(model.device) +).to("cuda") from transformers import TextStreamer _ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer)) @@ -368,12 +368,12 @@ def formatting_prompts_func(examples): # Merge and push to hub in mxfp4 4bit format if False: - model.save_pretrained_merged("finetuned_model", tokenizer, save_method="mxfp4") -if False: model.push_to_hub_merged("repo_id/repo_name", tokenizer, token="hf...", save_method="mxfp4") + model.save_pretrained_merged("finetuned_model", tokenizer, save_method = "mxfp4") +if False: model.push_to_hub_merged("repo_id/repo_name", tokenizer, token = "hf...", save_method = "mxfp4") # Merge and push to hub in 16bit if False: - model.save_pretrained_merged("finetuned_model", tokenizer, save_method="merged_16bit") + model.save_pretrained_merged("finetuned_model", tokenizer, save_method = "merged_16bit") if False: # Pushing to HF Hub model.push_to_hub_merged("hf/gpt-oss-finetune", tokenizer, save_method = "merged_16bit", token = "") From 936f815aa6b1d8c7c05aa193b384e2331d2d5114 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Mon, 13 Oct 2025 18:26:16 -0700 Subject: [PATCH 08/19] changes --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 184 ++++++++++++++++------------- 1 file changed, 105 insertions(+), 79 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index acb1d922..96f4399f 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -8,7 +8,7 @@ "
\n", "\n", "\n", - " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", @@ -51,7 +51,20 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "%%capture\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2" + "source": [ + "%%capture\n", + "!pip install --upgrade -qqq uv\n", + "try: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\n", + "except: get_numpy = \"numpy\"\n", + "!uv pip install -qqq \\\n", + " \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n", + " \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n", + " \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n", + " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", + "!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n", + "!uv pip install --no-deps trl==0.22.2\n", + "!uv pip install git+https://github.com/gradio-app/trackio.git@more-env" + ] }, { "cell_type": "markdown", @@ -210,8 +223,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n", + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2025.8.5: Fast Gpt_Oss patching. Transformers: 4.56.0.dev0.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.8.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.4.0\n", @@ -838,18 +851,18 @@ "\n", "reasoning language: French\n", "\n", - "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus r\u00e9centes. Tout d'abord, je dois v\u00e9rifier si j'ai acc\u00e8s \u00e0 des donn\u00e9es en temps r\u00e9el. \u00c9tant donn\u00e9 que je ne peux pas naviguer sur Internet ou acc\u00e9der directement \u00e0 l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils g\u00e9n\u00e9raux sur la fa\u00e7on de les trouver.\n", + "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.\n", "\n", - "Je devrais pr\u00e9ciser que les tendances Twitter \u00e9voluent rapidement et sont sp\u00e9cifiques \u00e0 chaque r\u00e9gion. Je pourrais sugg\u00e9rer de consulter la section \u00ab\u00a0En vogue\u00a0\u00bb sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient \u00eatre utiles. Il est important de souligner que les tendances varient selon la r\u00e9gion et l'heure de la journ\u00e9e. Je devrais garder un ton amical et bienveillant, peut-\u00eatre ajouter un emoji pour rester l\u00e9ger. Je vais structurer ma r\u00e9ponse \u00e9tape par \u00e9tape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des donn\u00e9es en temps r\u00e9el et proposer d'autres m\u00e9thodes. Je conserverai un langage simple et convivial, en \u00e9vitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", + "Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", "\n", - "1. **Open the \"Trending\" tab** on the Twitter app or website \u2013 it updates constantly! \n", - "2. **Search for hashtags** like #Trending or #Viral to see what\u2019s blowing up. \n", + "1. **Open the \"Trending\" tab** on the Twitter app or website – it updates constantly! \n", + "2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up. \n", "3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads). \n", - "4. **Check regional trends** \u2013 they often differ by location! \n", + "4. **Check regional trends** – they often differ by location! \n", "\n", "Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the \"Distracted Boyfriend\" meme revival), celebrity drama, or unexpected events (hello, weather disasters!). \n", "\n", - "Want me to brainstorm *what* might trend next? I\u2019ve got ideas!<|return|>\n" + "Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>\n" ] } ], @@ -879,7 +892,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We set some environment variables to customize the Trackio dashboard for experiment tracking\n", + "import os\n", + "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://example.com/logo_light.png\"\n", + "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://example.com/logo_dark.png\"\n", + "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -943,7 +969,7 @@ " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", - " report_to = \"none\", # Use this for WandB etc\n", + " report_to = \"trackio\",\n", " ),\n", ")" ] @@ -1376,7 +1402,7 @@ "\n", "reasoning language: French\n", "\n", - "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 \u2192 x^5 + 3x^\n" + "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 → x^5 + 3x^\n" ] } ], @@ -1451,7 +1477,7 @@ " \n", " \n", "\n", - " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "\n" ] } @@ -1781,9 +1807,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_83dd0a7d75d544f1a64fb265822b1dc6", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_28b1a6aef393405ba325d29e470b9332", - "value": "\u200722.8k/?\u2007[00:00<00:00,\u20071.88MB/s]" + "value": " 22.8k/? [00:00<00:00, 1.88MB/s]" } }, "0fc33d9d7b2e486ea16c7e9655d1f078": { @@ -1984,9 +2010,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_98122f1f5c974405aec8cee21d511235", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_60ee8e94b3794c6085a03a96058d03ee", - "value": "\u20074.00G/4.00G\u2007[00:47<00:00,\u2007171MB/s]" + "value": " 4.00G/4.00G [00:47<00:00, 171MB/s]" } }, "19983e4ce30944c7a57abfe01e463eb0": { @@ -2005,9 +2031,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9643968ed03642429372c2dac797031b", - 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"os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://example.com/logo_light.png\"\n", - "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://example.com/logo_dark.png\"\n", + "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"\n", + "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"\n", "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n" ] }, From f3684d019e5720037f4a8646e311da9beab99c7b Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Tue, 14 Oct 2025 13:59:48 -0700 Subject: [PATCH 10/19] Created using Colab --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 13757 ++++++++++++++------------- 1 file changed, 6891 insertions(+), 6866 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 8db64ddb..d2c50da8 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -1,6980 +1,7005 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", - "
\n", - "\n", - "\n", - " Join Discord if you need help + ⭐ Star us on Github ⭐\n", - "
\n", - "\n", - "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", - "\n", - "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### News" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n", - "\n", - "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n", - "\n", - "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n", - "\n", - "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", - "\n", - "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "!pip install --upgrade -qqq uv\n", - "try: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\n", - "except: get_numpy = \"numpy\"\n", - "!uv pip install -qqq \\\n", - " \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n", - " \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n", - " \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n", - " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", - "!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n", - "!uv pip install --no-deps trl==0.22.2\n", - "!uv pip install git+https://github.com/gradio-app/trackio.git@more-env" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NJq3z_gYnVgd" - }, - "source": [ - "### Unsloth" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r2v_X2fA0Df5" - }, - "source": [ - "We're about to demonstrate the power of the new OpenAI GPT-OSS 20B model through a finetuning example. To use our `MXFP4` inference example, use this [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_(20B)-Inference.ipynb) instead." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 527, - "referenced_widgets": [ - "8c039ec5fb594077aa9947c2683ca1ef", - "73107ec68ea84a12914293008d2f2cd9", - "18524360ea164f8794178e7dd4ece59c", - "9990ddfd1aa94f07b43545d1c8bca2b4", - "22c213a5fb574eeea5f9a7efab5b1ba7", - "b372d6ed1c204203be1fac53f2093c62", - "376cd15963c84026a4ba2a2c212b813e", - "38f281294af847129355dfa86416ae0c", - "aa5d182dec464a709c6f3ce95b415304", - "8f39efb61c224ae18db657ce38efd085", - "2a2612b9d72c49089ebb79bb28c0c415", - "607d1555851348b7813f6a3db1844109", - "c4e07ba599fc462792e39b6f3841ec46", - "29d35da050f94c17a8b09331e16d9c23", - "19983e4ce30944c7a57abfe01e463eb0", - "a0713b54fa2b47c2b726042051640522", - "f1237a5c19014663b8ec6475ff81091d", - 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"output_type": "stream", - "text": [ - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "🦥 Unsloth Zoo will now patch everything to make training faster!\n", - "==((====))== Unsloth 2025.8.5: Fast Gpt_Oss patching. Transformers: 4.56.0.dev0.\n", - " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", - "O^O/ \\_/ \\ Torch: 2.8.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.4.0\n", - "\\ / Bfloat16 = FALSE. FA [Xformers = None. FA2 = False]\n", - " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", - "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n", - "Unsloth: Using float16 precision for gpt_oss won't work! Using float32.\n" - ] - }, + "cells": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8c039ec5fb594077aa9947c2683ca1ef", - "version_major": 2, - "version_minor": 0 + "cell_type": "markdown", + "metadata": { + "id": "DajGjqXnhQUk" }, - "text/plain": [ - "model.safetensors.index.json: 0.00B [00:00, ?B/s]" + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + "\n", + "\n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "607d1555851348b7813f6a3db1844109", - "version_major": 2, - "version_minor": 0 + "cell_type": "markdown", + "metadata": { + "id": "2ClMzZV3hQUm" }, - "text/plain": [ - "model-00001-of-00004.safetensors: 0%| | 0.00/4.00G [00:00=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n", + " \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n", + " \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n", + " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", + "!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n", + "!uv pip install --no-deps trl==0.22.2\n", + "!uv pip install git+https://github.com/gradio-app/trackio.git@more-env" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "323c5d1ee6fd4fc99951adda4afb572c", - "version_major": 2, - "version_minor": 0 + "cell_type": "markdown", + "metadata": { + "id": "NJq3z_gYnVgd" }, - "text/plain": [ - "Loading checkpoint shards: 0%| | 0/4 [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "54608166730a4e4aa836a2588faa0f5b", - "version_major": 2, - "version_minor": 0 + "cell_type": "markdown", + "metadata": { + "id": "4-sFShVvnVgg" }, - "text/plain": [ - "chat_template.jinja: 0.00B [00:00, ?B/s]" + "source": [ + "### Reasoning Effort\n", + "The `gpt-oss` models from OpenAI include a feature that allows users to adjust the model's \"reasoning effort.\" This gives you control over the trade-off between the model's performance and its response speed (latency) which by the amount of token the model will use to think.\n", + "\n", + "----\n", + "\n", + "The `gpt-oss` models offer three distinct levels of reasoning effort you can choose from:\n", + "\n", + "* **Low**: Optimized for tasks that need very fast responses and don't require complex, multi-step reasoning.\n", + "* **Medium**: A balance between performance and speed.\n", + "* **High**: Provides the strongest reasoning performance for tasks that require it, though this results in higher latency." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from unsloth import FastLanguageModel\n", - "import torch\n", - "max_seq_length = 1024\n", - "dtype = None\n", - "\n", - "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", - "fourbit_models = [\n", - " \"unsloth/gpt-oss-20b-unsloth-bnb-4bit\", # 20B model using bitsandbytes 4bit quantization\n", - " \"unsloth/gpt-oss-120b-unsloth-bnb-4bit\",\n", - " \"unsloth/gpt-oss-20b\", # 20B model using MXFP4 format\n", - " \"unsloth/gpt-oss-120b\",\n", - "] # More models at https://huggingface.co/unsloth\n", - "\n", - "model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"unsloth/gpt-oss-20b\",\n", - " dtype = dtype, # None for auto detection\n", - " max_seq_length = max_seq_length, # Choose any for long context!\n", - " load_in_4bit = True, # 4 bit quantization to reduce memory\n", - " full_finetuning = False, # [NEW!] We have full finetuning now!\n", - " # token = \"hf_...\", # use one if using gated models\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rVqtZVxxnVgf" - }, - "source": [ - "We now add LoRA adapters for parameter efficient finetuning - this allows us to only efficiently train 1% of all parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "f_LK81NRnVgg", - "outputId": "5243c491-bc32-4a97-f326-fa6862da448a" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unsloth: Making `model.base_model.model.model` require gradients\n" - ] - } - ], - "source": [ - "model = FastLanguageModel.get_peft_model(\n", - " model,\n", - " r = 8, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n", - " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", - " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", - " lora_alpha = 16,\n", - " lora_dropout = 0, # Supports any, but = 0 is optimized\n", - " bias = \"none\", # Supports any, but = \"none\" is optimized\n", - " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", - " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", - " random_state = 3407,\n", - " use_rslora = False, # We support rank stabilized LoRA\n", - " loftq_config = None, # And LoftQ\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4-sFShVvnVgg" - }, - "source": [ - "### Reasoning Effort\n", - "The `gpt-oss` models from OpenAI include a feature that allows users to adjust the model's \"reasoning effort.\" This gives you control over the trade-off between the model's performance and its response speed (latency) which by the amount of token the model will use to think.\n", - "\n", - "----\n", - "\n", - "The `gpt-oss` models offer three distinct levels of reasoning effort you can choose from:\n", - "\n", - "* **Low**: Optimized for tasks that need very fast responses and don't require complex, multi-step reasoning.\n", - "* **Medium**: A balance between performance and speed.\n", - "* **High**: Provides the strongest reasoning performance for tasks that require it, though this results in higher latency." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yxCi64FnnVgh", + "outputId": "26150958-7208-4dbd-ce07-bdac6748465b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: low\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>Equation: x^5 + 3x^4 - 10 = 3. So x^5 + 3x^4 - 13 =0. Solve for real roots? maybe numeric. Let's try approximate.\n", + "\n", + "We can test integer roots: try x=1 => 1+3\n" + ] + } + ], + "source": [ + "from transformers import TextStreamer\n", + "\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n", + ").to(\"cuda\")\n", + "\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] }, - "id": "yxCi64FnnVgh", - "outputId": "26150958-7208-4dbd-ce07-bdac6748465b" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: low\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>Equation: x^5 + 3x^4 - 10 = 3. So x^5 + 3x^4 - 13 =0. Solve for real roots? maybe numeric. Let's try approximate.\n", - "\n", - "We can test integer roots: try x=1 => 1+3\n" - ] - } - ], - "source": [ - "from transformers import TextStreamer\n", - "\n", - "messages = [\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", - "\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IlAzq_RinVgh" - }, - "source": [ - "Changing the `reasoning_effort` to `medium` will make the model think longer. We have to increase the `max_new_tokens` to occupy the amount of the generated tokens but it will give better and more correct answer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "IlAzq_RinVgh" + }, + "source": [ + "Changing the `reasoning_effort` to `medium` will make the model think longer. We have to increase the `max_new_tokens` to occupy the amount of the generated tokens but it will give better and more correct answer" + ] }, - "id": "kaPPyXN1nVgh", - "outputId": "ff594b71-a82c-4203-fa6e-f9fd14b210a0" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: medium\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The user: \"Solve x^5 + 3x^4 - 10 = 3.\" Wait maybe it's an equation: x^5 + 3x^4 - 10 = 3. The variable x unknown. Solve for x. We need to solve the equation:\n", - "\n", - "x^\n" - ] - } - ], - "source": [ - "from transformers import TextStreamer\n", - "\n", - "messages = [\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", - "\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M0iuyJt7nVgh" - }, - "source": [ - "Lastly we will test it using `reasoning_effort` to `high`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kaPPyXN1nVgh", + "outputId": "ff594b71-a82c-4203-fa6e-f9fd14b210a0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: medium\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The user: \"Solve x^5 + 3x^4 - 10 = 3.\" Wait maybe it's an equation: x^5 + 3x^4 - 10 = 3. The variable x unknown. Solve for x. We need to solve the equation:\n", + "\n", + "x^\n" + ] + } + ], + "source": [ + "from transformers import TextStreamer\n", + "\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n", + ").to(\"cuda\")\n", + "\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] }, - "id": "QrjUXjN8nVgh", - "outputId": "9db0a3e3-5aae-40b6-8acb-a9b393d0d176" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: high\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation: x^5 + 3x^4 - 10 = 3. Or maybe it's x^5 + 3x^4 - 10 = 3? That seems like a polynomial equation: x^5 + 3x^4 - 10\n" - ] - } - ], - "source": [ - "from transformers import TextStreamer\n", - "\n", - "messages = [\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", - "\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "e6BnnYcbnVgh" - }, - "source": [ - "\n", - "### Data Prep" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "91gfk9L3nVgh" - }, - "source": [ - "The `HuggingFaceH4/Multilingual-Thinking` dataset will be utilized as our example. This dataset, available on Hugging Face, contains reasoning chain-of-thought examples derived from user questions that have been translated from English into four other languages. It is also the same dataset referenced in OpenAI's [cookbook](https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers) for fine-tuning. The purpose of using this dataset is to enable the model to learn and develop reasoning capabilities in these four distinct languages." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 183, - "referenced_widgets": [ - "8b8eb63337fb428fb0702ab599e2d402", - "ec16474c3bb2416ea72cda7801911a36", - "ba78a415e8b8469ea3ca3f4f5fe2d419", - "2a4965d875f640cf8a10998614308c10", - "b6bbb3fd3245428c9a56ccb007bdd1ab", - "f14e045ddcf54eef958e92c7a8616d50", - "8d0635071af84cf1ac18e9a052087e32", - "d1c64a303c6541f4a5463748383cecc1", - "470ed5fc391f4c8fbe4d4f07d5aa3e23", - "a9bd7392477840acbab43d9263955647", - "0017ec22a7504941934db02a385dce85", - "9a430ca8b86e4f279122b45267a038c0", - "e75b2c318d464bb8b4debc68621cb533", - "907f9b49253f46638f2c1ecc79116698", - "8e7481889c1d4d70bbf4f5b0dc849bdc", - "ad65c3013d2d4cedba1fd98ef835b3b5", - "e3a9a9b8868e40c3b754b4fb6a299906", - "29f0d621132742188596ce3a7dfb1704", - "737f0b3c8edd40c69ac7025c6ee00723", - "7e5c3cad61f9447dbfdc25e3487223b7", - "297f17e5d1e743c7acea1d15731d255e", - "30893988a2a4460696d92911a4ebede7", - "c13b432ba06341c09746c52307f866aa", - "1bce340c0f8848fe85db3beaf8dc1ed7", - "8f3aa28ce7c14c3a97629855721d0c25", - "62fafca550a7466fb478a161a1e5c541", - "4f93b270ee7b4eec95113b56214eada8", - "03a7eaea40cf4eb69b0f0d1e495e631c", - "3986d3adb14d48e1b5939e68f9d3ffc5", - "8cb4d60568bf4572a37870b8a1b510b2", - "97e57af4fdd84d8baeb52fea57b3ab14", - "602a471c56e54731a847d1b29f72e999", - "9518e8ada50747818ad94bf81118a964" - ] + "cell_type": "markdown", + "metadata": { + "id": "M0iuyJt7nVgh" + }, + "source": [ + "Lastly we will test it using `reasoning_effort` to `high`" + ] }, - "id": "62QfuPXBnVgi", - "outputId": "dfe615ff-591a-4a3d-fb3f-3198626cdd6b" - }, - "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8b8eb63337fb428fb0702ab599e2d402", - "version_major": 2, - "version_minor": 0 + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QrjUXjN8nVgh", + "outputId": "9db0a3e3-5aae-40b6-8acb-a9b393d0d176" }, - "text/plain": [ - "README.md: 0.00B [00:00, ?B/s]" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: high\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation: x^5 + 3x^4 - 10 = 3. Or maybe it's x^5 + 3x^4 - 10 = 3? That seems like a polynomial equation: x^5 + 3x^4 - 10\n" + ] + } + ], + "source": [ + "from transformers import TextStreamer\n", + "\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n", + ").to(\"cuda\")\n", + "\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9a430ca8b86e4f279122b45267a038c0", - "version_major": 2, - "version_minor": 0 + "cell_type": "markdown", + "metadata": { + "id": "e6BnnYcbnVgh" }, - "text/plain": [ - "data/train-00000-of-00001.parquet: 0%| | 0.00/5.29M [00:00\n", + "### Data Prep" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c13b432ba06341c09746c52307f866aa", - "version_major": 2, - "version_minor": 0 + "cell_type": "markdown", + "metadata": { + "id": "91gfk9L3nVgh" }, - "text/plain": [ - "Generating train split: 0%| | 0/1000 [00:00system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: medium\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", - "\n", - "reasoning language: French\n", - "\n", - "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.\n", - "\n", - "Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", - "\n", - "1. **Open the \"Trending\" tab** on the Twitter app or website – it updates constantly! \n", - "2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up. \n", - "3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads). \n", - "4. **Check regional trends** – they often differ by location! \n", - "\n", - "Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the \"Distracted Boyfriend\" meme revival), celebrity drama, or unexpected events (hello, weather disasters!). \n", - "\n", - "Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>\n" - ] - } - ], - "source": [ - "print(dataset[0]['text'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tQ3i-AMFnVgj" - }, - "source": [ - "What is unique about GPT-OSS is that it uses OpenAI [Harmony](https://github.com/openai/harmony) format which support conversation structures, reasoning output, and tool calling." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Rtdsxyl6nVgk" - }, - "source": [ - "\n", - "### Train the model\n", - "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We set some environment variables to customize the Trackio dashboard for experiment tracking\n", - "import os\n", - "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"\n", - "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"\n", - "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 67, - "referenced_widgets": [ - "0fc33d9d7b2e486ea16c7e9655d1f078", - "c4568dd761a140b6bb9d5996a98a22d4", - "587e44e5af14403582c0b87ef85813b4", - "1adeb75bbdaa4ef388c82f786916509a", - "1cce8185eab94b189fee6a7efb0eb3dc", - "4362a20e703c42d4b0b92dc410d62889", - "227eab802b6543d8b6915da6fed18c6e", - "bbd94cb3957e4b0b9fde5ef117753d43", - "17ee69f3ffdd4985b436803c99a80b3d", - "5df9512f00d842d5bba5da9f97d703ac", - "aa886d9ac13d40c2a90625943b782168" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "97ef826a71cf4db6b2487e3ceb610574", + "5d2597a3407840eeae41ad02a008eae2", + "33296d2012e3437dac6393b1e447d89a", + "fd92fe1fac8245faad1d0b4df340eacd", + "34380cffc7ac48908baaa8103d26b952", + "f1cb00038b094d079dd924ce3c523a2c", + "04b1a6ba8ec54e6d8ff2f9406d0e708f", + "ad39a8481898489b858c2e797faa564a", + "322de8a1e48a4c7bbe033561f12191de", + "04bc14d9112242259867abad6efc53c3", + "2e9287b93e93412b9f2b12cd98d69ab6" + ] + }, + "id": "FW-l11GBnVgj", + "outputId": "ebb65aba-e7d8-4873-99cd-b2b17cc994ad" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "97ef826a71cf4db6b2487e3ceb610574", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/1000 [00:00system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: medium\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", + "\n", + "reasoning language: French\n", + "\n", + "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.\n", + "\n", + "Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", + "\n", + "1. **Open the \"Trending\" tab** on the Twitter app or website – it updates constantly! \n", + "2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up. \n", + "3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads). \n", + "4. **Check regional trends** – they often differ by location! \n", + "\n", + "Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the \"Distracted Boyfriend\" meme revival), celebrity drama, or unexpected events (hello, weather disasters!). \n", + "\n", + "Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>\n" + ] + } + ], + "source": [ + "print(dataset[0]['text'])" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from trl import SFTConfig, SFTTrainer\n", - "trainer = SFTTrainer(\n", - " model = model,\n", - " tokenizer = tokenizer,\n", - " train_dataset = dataset,\n", - " args = SFTConfig(\n", - " per_device_train_batch_size = 1,\n", - " gradient_accumulation_steps = 4,\n", - " warmup_steps = 5,\n", - " # num_train_epochs = 1, # Set this for 1 full training run.\n", - " max_steps = 30,\n", - " learning_rate = 2e-4,\n", - " logging_steps = 1,\n", - " optim = \"adamw_8bit\",\n", - " weight_decay = 0.01,\n", - " lr_scheduler_type = \"linear\",\n", - " seed = 3407,\n", - " output_dir = \"outputs\",\n", - " report_to = \"trackio\",\n", - " ),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs. This helps increase accuracy of finetunes and lower loss as well!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from unsloth.chat_templates import train_on_responses_only\n", - "\n", - "gpt_oss_kwargs = dict(instruction_part = \"<|start|>user<|message|>\", response_part=\"<|start|>assistant<|channel|>final<|message|>\")\n", - "\n", - "trainer = train_on_responses_only(\n", - " trainer,\n", - " **gpt_oss_kwargs,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's verify masking the instruction part is done! Let's print the 100th row again." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's print the masked out example - you should see only the answer is present:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100][\"labels\"]]).replace(tokenizer.pad_token, \" \")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GPU = Tesla T4. Max memory = 14.741 GB.\n", - "12.811 GB of memory reserved.\n" - ] - } - ], - "source": [ - "# @title Show current memory stats\n", - "gpu_stats = torch.cuda.get_device_properties(0)\n", - "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", - "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", - "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", - "print(f\"{start_gpu_memory} GB of memory reserved.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 }, - "id": "aFaejiSonVgk", - "outputId": "f9768c59-df45-4b80-b150-2e99036837ae" - }, - "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", - "The tokenizer has new special tokens that are also defined in the model configs. The model configs were aligned accordingly. Updated tokens: {'bos_token_id': 199998, 'pad_token_id': 200017}\n", - "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", - " \\\\ /| Num examples = 1,000 | Num Epochs = 1 | Total steps = 30\n", - "O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n", - "\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n", - " \"-____-\" Trainable parameters = 3,981,312 of 20,918,738,496 (0.02% trained)\n", - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "tQ3i-AMFnVgj" + }, + "source": [ + "What is unique about GPT-OSS is that it uses OpenAI [Harmony](https://github.com/openai/harmony) format which support conversation structures, reasoning output, and tool calling." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unsloth: Will smartly offload gradients to save VRAM!\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "Rtdsxyl6nVgk" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`." + ] }, { - "data": { - "text/html": [ - "\n", - "
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StepTraining Loss
12.130500
22.918100
32.419300
42.167900
51.978200
62.119900
71.825800
81.703400
91.974400
101.796700
111.698900
121.637100
131.633600
141.570100
151.418700
161.643800
171.697200
181.830000
191.386500
201.400800
211.329000
221.382800
231.504600
241.589200
251.400000
261.431400
271.465200
281.468800
291.421100
301.408200

" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "0fc33d9d7b2e486ea16c7e9655d1f078", + "c4568dd761a140b6bb9d5996a98a22d4", + "587e44e5af14403582c0b87ef85813b4", + "1adeb75bbdaa4ef388c82f786916509a", + "1cce8185eab94b189fee6a7efb0eb3dc", + "4362a20e703c42d4b0b92dc410d62889", + "227eab802b6543d8b6915da6fed18c6e", + "bbd94cb3957e4b0b9fde5ef117753d43", + "17ee69f3ffdd4985b436803c99a80b3d", + "5df9512f00d842d5bba5da9f97d703ac", + "aa886d9ac13d40c2a90625943b782168" + ] + }, + "id": "O-XZLeLYnVgk", + "outputId": "1ffe6822-e7a2-4c69-c764-59933ef359ca" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unsloth: Switching to float32 training since model cannot work with float16\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0fc33d9d7b2e486ea16c7e9655d1f078", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Unsloth: Tokenizing [\"text\"] (num_proc=2): 0%| | 0/1000 [00:00" + "source": [ + "from trl import SFTConfig, SFTTrainer\n", + "import os\n", + "\n", + "# Set some environment variables to customize the Trackio dashboard for experiment tracking\n", + "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"\n", + "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"\n", + "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n", + "\n", + "\n", + "trainer = SFTTrainer(\n", + " model = model,\n", + " tokenizer = tokenizer,\n", + " train_dataset = dataset,\n", + " args = SFTConfig(\n", + " per_device_train_batch_size = 1,\n", + " gradient_accumulation_steps = 4,\n", + " warmup_steps = 5,\n", + " # num_train_epochs = 1, # Set this for 1 full training run.\n", + " max_steps = 30,\n", + " learning_rate = 2e-4,\n", + " logging_steps = 1,\n", + " optim = \"adamw_8bit\",\n", + " weight_decay = 0.01,\n", + " lr_scheduler_type = \"linear\",\n", + " seed = 3407,\n", + " output_dir = \"outputs\",\n", + " report_to = \"trackio\",\n", + " ),\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "trainer_stats = trainer.train()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "_G3eBV3EnVgk", - "outputId": "7c86ff1e-b5b5-47f6-bbc4-eec30a219e46" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "645.6936 seconds used for training.\n", - "10.76 minutes used for training.\n", - "Peak reserved memory = 12.975 GB.\n", - "Peak reserved memory for training = 0.164 GB.\n", - "Peak reserved memory % of max memory = 88.02 %.\n", - "Peak reserved memory for training % of max memory = 1.113 %.\n" - ] - } - ], - "source": [ - "# @title Show final memory and time stats\n", - "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", - "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", - "used_percentage = round(used_memory / max_memory * 100, 3)\n", - "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", - "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", - "print(\n", - " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", - ")\n", - "print(f\"Peak reserved memory = {used_memory} GB.\")\n", - "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", - "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", - "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CuK0hVOsnVgk" - }, - "source": [ - "\n", - "### Inference\n", - "Let's run the model! You can change the instruction and input - leave the output blank!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "YJ08d_vDhQUq" + }, + "source": [ + "We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs. This helps increase accuracy of finetunes and lower loss as well!" + ] }, - "id": "RdVCmTuBnVgl", - "outputId": "266de72f-20d3-4253-fffe-6b2764a5a7d9" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: medium\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", - "\n", - "reasoning language: French\n", - "\n", - "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The equation is \\(x^5 + 3x^4 - 10 = 3\\), or \\(x^5 + 3x^4 - 13 = 0\\). So we need to find the roots of \\(x^5 + 3x^4 - 13\n" - ] - } - ], - "source": [ - "messages = [\n", - " {\"role\": \"system\", \"content\": \"reasoning language: French\\n\\nYou are a helpful assistant that can solve mathematical problems.\"},\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"medium\",\n", - ").to(\"cuda\")\n", - "from transformers import TextStreamer\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5e1j8KRb4AwO" - }, - "source": [ - "\n", - "### Saving, loading finetuned models\n", - "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", - "\n", - "**[NOTE]** Currently finetunes can only be loaded via Unsloth in the meantime - we're working on vLLM and GGUF exporting!" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "Ds7ByU7e4KF7" - }, - "outputs": [], - "source": [ - "model.save_pretrained(\"finetuned_model\")\n", - "# model.push_to_hub(\"hf_username/finetuned_model\", token = \"hf_...\") # Save to HF" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ELyXzRpl4hr0" - }, - "source": [ - "To run the finetuned model, you can do the below after setting `if False` to `if True` in a new instance." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ywSGjLVxhQUq" + }, + "outputs": [], + "source": [ + "from unsloth.chat_templates import train_on_responses_only\n", + "\n", + "gpt_oss_kwargs = dict(instruction_part = \"<|start|>user<|message|>\", response_part=\"<|start|>assistant<|channel|>final<|message|>\")\n", + "\n", + "trainer = train_on_responses_only(\n", + " trainer,\n", + " **gpt_oss_kwargs,\n", + ")" + ] }, - "id": "kCMDSxvD4SKu", - "outputId": "dbf449e3-d794-490c-dc1f-a2b9afdb93ef" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: high\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", - "\n", - "reasoning language: French\n", - "\n", - "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 → x^5 + 3x^\n" - ] - } - ], - "source": [ - "if False:\n", - " from unsloth import FastLanguageModel\n", - " model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"finetuned_model\", # YOUR MODEL YOU USED FOR TRAINING\n", - " max_seq_length = 1024,\n", - " dtype = None,\n", - " load_in_4bit = True,\n", - " )\n", - "\n", - "messages = [\n", - " {\"role\": \"system\", \"content\": \"reasoning language: French\\n\\nYou are a helpful assistant that can solve mathematical problems.\"},\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"high\",\n", - ").to(\"cuda\")\n", - "from transformers import TextStreamer\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Saving to float16 for VLLM or mxfp4\n", - "\n", - "We also support saving to `float16` or `mxfp4` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge and push to hub in mxfp4 4bit format\n", - "if False:\n", - " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"mxfp4\")\n", - "if False: model.push_to_hub_merged(\"repo_id/repo_name\", tokenizer, token = \"hf...\", save_method = \"mxfp4\")\n", - "\n", - "# Merge and push to hub in 16bit\n", - "if False:\n", - " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"merged_16bit\")\n", - "if False: # Pushing to HF Hub\n", - " model.push_to_hub_merged(\"hf/gpt-oss-finetune\", tokenizer, save_method = \"merged_16bit\", token = \"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qMNviX7XnVgl" - }, - "source": [ - "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", - "\n", - "Some other links:\n", - "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n", - "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", - "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", - "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", - "\n", - "

\n", - " \n", - " \n", - " \n", - "\n", - " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", - "
\n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.13.7" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "0017ec22a7504941934db02a385dce85": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "024cba3b43c840238940ef161521c7cb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - 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Max memory = {max_memory} GB.\")\n", + "print(f\"{start_gpu_memory} GB of memory reserved.\")" + ] }, - "ae71957fa4f04efb9e8f207f1d9de48c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } + { + "cell_type": "markdown", + "metadata": { + "id": "W5VwHZCshQUq" + }, + "source": [ + "Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`" + ] }, - "b1683c2194bf4d34bd61434fcca06c32": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d73ccf7259b9439299a1d17cd22b822b", - "IPY_MODEL_ad6e28f080ef4ee8bb6ec726669df8c5", - "IPY_MODEL_0c95cd53486241a689301dee6bd3c2d3" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "aFaejiSonVgk", + "outputId": "f9768c59-df45-4b80-b150-2e99036837ae" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "The tokenizer has new special tokens that are also defined in the model configs. The model configs were aligned accordingly. Updated tokens: {'bos_token_id': 199998, 'pad_token_id': 200017}\n", + "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", + " \\\\ /| Num examples = 1,000 | Num Epochs = 1 | Total steps = 30\n", + "O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n", + "\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n", + " \"-____-\" Trainable parameters = 3,981,312 of 20,918,738,496 (0.02% trained)\n", + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unsloth: Will smartly offload gradients to save VRAM!\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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StepTraining Loss
12.130500
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51.978200
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You can change the instruction and input - leave the output blank!" + ] }, - "c13b432ba06341c09746c52307f866aa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1bce340c0f8848fe85db3beaf8dc1ed7", - "IPY_MODEL_8f3aa28ce7c14c3a97629855721d0c25", - "IPY_MODEL_62fafca550a7466fb478a161a1e5c541" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RdVCmTuBnVgl", + "outputId": "266de72f-20d3-4253-fffe-6b2764a5a7d9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: medium\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", + "\n", + "reasoning language: French\n", + "\n", + "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The equation is \\(x^5 + 3x^4 - 10 = 3\\), or \\(x^5 + 3x^4 - 13 = 0\\). So we need to find the roots of \\(x^5 + 3x^4 - 13\n" + ] + } ], - "layout": "IPY_MODEL_4f93b270ee7b4eec95113b56214eada8" - } - }, - "c4568dd761a140b6bb9d5996a98a22d4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4362a20e703c42d4b0b92dc410d62889", - "placeholder": "​", - "style": "IPY_MODEL_227eab802b6543d8b6915da6fed18c6e", - "value": "Unsloth: Tokenizing ["text"] (num_proc=2): 100%" - } - }, - "c4e07ba599fc462792e39b6f3841ec46": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - 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10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"medium\",\n", + ").to(\"cuda\")\n", + "from transformers import TextStreamer\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] }, - "d307e2839dae4480b07e25b1db2ff9e1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_83fd58564b7d46c38cff553df21a69c6", - "max": 27868174, - 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Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", + "\n", + "reasoning language: French\n", + "\n", + "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 → x^5 + 3x^\n" + ] + } ], - "layout": "IPY_MODEL_dd8e22c3182a486b968acfb24757a567" - } - }, - "dd8e22c3182a486b968acfb24757a567": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - 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10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"high\",\n", + ").to(\"cuda\")\n", + "from transformers import TextStreamer\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] }, - "fd92fe1fac8245faad1d0b4df340eacd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_04bc14d9112242259867abad6efc53c3", - "placeholder": "​", - "style": "IPY_MODEL_2e9287b93e93412b9f2b12cd98d69ab6", - "value": " 1000/1000 [00:00<00:00, 1151.76 examples/s]" - } + { + "cell_type": "markdown", + "metadata": { + "id": "y5u-_HjqhQU3" + }, + "source": [ + "### Saving to float16 for VLLM or mxfp4\n", + "\n", + "We also support saving to `float16` or `mxfp4` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] }, - "ff74e51179ab471b898e11008c91629e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_72de77c20e1c4e3982aefb8a6868fed6", - "max": 165, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_ccb4d40f2ede4676a334aed9855aabf7", - "value": 165 - } + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HHEXm8jlhQU3" + }, + "outputs": [], + "source": [ + "# Merge and push to hub in mxfp4 4bit format\n", + "if False:\n", + " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"mxfp4\")\n", + "if False: model.push_to_hub_merged(\"repo_id/repo_name\", tokenizer, token = \"hf...\", save_method = \"mxfp4\")\n", + "\n", + "# Merge and push to hub in 16bit\n", + "if False:\n", + " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"merged_16bit\")\n", + "if False: # Pushing to HF Hub\n", + " model.push_to_hub_merged(\"hf/gpt-oss-finetune\", tokenizer, save_method = \"merged_16bit\", token = \"\")" + ] }, - "state": {} - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + { + "cell_type": "markdown", + "metadata": { + "id": "qMNviX7XnVgl" + }, + "source": [ + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", + "
\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.13.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0017ec22a7504941934db02a385dce85": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "024cba3b43c840238940ef161521c7cb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": 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\n", - "\n", - "\n", - " Join Discord if you need help + ⭐ Star us on Github ⭐\n", - "
\n", - "\n", - "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", - "\n", - "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + "\n", + "\n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n", + "\n", + "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n", + "\n", + "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n", + "\n", + "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install --upgrade -qqq uv\n", + "try: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\n", + "except: get_numpy = \"numpy\"\n", + "!uv pip install -qqq \\\n", + " \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n", + " \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n", + " \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n", + " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", + "!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n", + "!uv pip install --no-deps trl==0.22.2\n", + "!uv pip install git+https://github.com/gradio-app/trackio.git@more-env" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NJq3z_gYnVgd" + }, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r2v_X2fA0Df5" + }, + "source": [ + "We're about to demonstrate the power of the new OpenAI GPT-OSS 20B model through a finetuning example. To use our `MXFP4` inference example, use this [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_(20B)-Inference.ipynb) instead." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527, + "referenced_widgets": [ + "8c039ec5fb594077aa9947c2683ca1ef", + "73107ec68ea84a12914293008d2f2cd9", + "18524360ea164f8794178e7dd4ece59c", + "9990ddfd1aa94f07b43545d1c8bca2b4", + "22c213a5fb574eeea5f9a7efab5b1ba7", + "b372d6ed1c204203be1fac53f2093c62", + "376cd15963c84026a4ba2a2c212b813e", + "38f281294af847129355dfa86416ae0c", + "aa5d182dec464a709c6f3ce95b415304", + "8f39efb61c224ae18db657ce38efd085", + "2a2612b9d72c49089ebb79bb28c0c415", + "607d1555851348b7813f6a3db1844109", + "c4e07ba599fc462792e39b6f3841ec46", + "29d35da050f94c17a8b09331e16d9c23", + "19983e4ce30944c7a57abfe01e463eb0", + "a0713b54fa2b47c2b726042051640522", + "f1237a5c19014663b8ec6475ff81091d", + 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"f999d6c9069249b9ae9e1a32a3a0a80f", + "28bf8cd1a1f04fb099ffc36700ead6ad", + "c5996543c5c346a99000c70e810f8e8c", + "477177141b7349e9b3e01fdfd845bfbb", + "686d7f8f60554cdba30eeda79db4501f", + "ba25ca3bc967493c8d9f53670d6245b9", + "bd365bd853fd417aa7b7096ea1e9540c", + "fd15ab7222824c9abcce3a17cc0209af", + "b2df64020b764343914f9acc97d86076", + "1b7009babefe4108be77c969c97c6c56", + "2e560b107cbf4f9ea1b34bf3a3094678", + "74ebde2ac07d49f0ba65b7d70cea09f1", + "323c5d1ee6fd4fc99951adda4afb572c", + "444905088dc045faa382e6fdec70574a", + "65f08647736f42c285980f4580b8c3f2", + "ddd55b7ba1164e809f9406bf2f9de9a4", + "09d35ff962e24e0791932a5d60a8a911", + "6db32b388f734fd598644ddfef4632f1", + "50baccf35989487f9bc9049ff4303f4d", + "479f8e8afeab4bc3ac20363e7dfef770", + "8ec51fbe49f74f82b0f13c658f5d6bf8", + "8d925b65a79240f0bad9cd8add2bfec7", + "cb7de23470ce4dbbbb3a636d1aa0af9c", + "14dd75fc40d94565b05931f6d9519b8a", + "6378d55aada8467688da8d1da0c123ce", + "ff74e51179ab471b898e11008c91629e", + "3821f16f51ab4f3ebedb06c94d3846ce", + "1f050ac26f114a36b2c8fbf810084bf5", + "99dfd860e52240838e9c55238884fcee", + "a17f3673fb6c4971bd53489a80c12b03", + "72de77c20e1c4e3982aefb8a6868fed6", + "ccb4d40f2ede4676a334aed9855aabf7", + "54355aab70f34cbc8465048d8cdd8cf2", + "6c279fe5cb444673a65f1caba4648fc4", + "b1683c2194bf4d34bd61434fcca06c32", + "d73ccf7259b9439299a1d17cd22b822b", + "ad6e28f080ef4ee8bb6ec726669df8c5", + "0c95cd53486241a689301dee6bd3c2d3", + "ecb9b5a306cc4244a12f8bdd7c65e498", + "add72aaf688a4ad8bfe7b5ffda08d21d", + "70f86aee84a143159feded54e0b0e2ee", + "d22ce9627bdf41f59e74bd46c8e0d921", + "024cba3b43c840238940ef161521c7cb", + "83dd0a7d75d544f1a64fb265822b1dc6", + "28b1a6aef393405ba325d29e470b9332", + "157cdd563d2145388b8288d7ed981f6f", + "d302c13ddea44894bad6494309771580", + "d307e2839dae4480b07e25b1db2ff9e1", + "8cd9481d509d40d398acda0fe597c999", + "bb816edcb65640688306f1b099a1a088", + "c0615e2ed6c246d3bd64e50002f1b5cf", + "72986da11c5c400b8f3fcf73cebf8af8", + "83fd58564b7d46c38cff553df21a69c6", + "75ead08eb8124736800f59c455785cba", + "040250e6afb74feeb107c69e50a985bc", + "5d6c9f818ec94c5d9f8b325839371963", + "097cf7aa8f4344dd84af6021e12ee829", + "7322a242ad4744168de44963be435725", + "30fd12adf3a14aad813b0d9b29670596", + "3a1670c82c4544578816944852a3a48f", + "fd443c983f1a409aa6be506aea521e9a", + "88e50815be2a48e2a434b78ea4b98bd2", + "5e42a9d44ffe44eebf95d3bc0fd0f752", + "bb24af1cff464e35912adcb7fb2bd070", + "332a4aedcef1459b8a553a9c8a27a72d", + "3f023c6bb6604ae9b4c6eea1fd12a905", + "1be08746d9294ea49380a48182acfaa1", + "54608166730a4e4aa836a2588faa0f5b", + "e75762e5993c440da2c0fb38056a56c4", + "1447cc59ce834e9b950c9f78d557f11c", + "7b312cbc61c342eda30999be93bda78b", + "57f520767b4a4cc2bfe993457f9f6799", + "11ada4258a894a27a4e096257ecac8ff", + "f527df8dc8734cbcac2bfe27faaa7dfa", + "b1cdcb9c0b9a463bbbc4a16b64f24e12", + "0c6c7e5a315e44c0a545515626ef3606", + "6d1644394190402baf9a58b00b1b3de8", + "3c88be2e8d5b4559b7c1928e7a46e847" + ] }, + "id": "QmUBVEnvCDJv", + "outputId": "62fa5df0-0119-443b-84b8-ecc19401ee3b" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "2ClMzZV3hQUm" - }, - "source": [ - "### News" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "🦥 Unsloth Zoo will now patch everything to make training faster!\n", + "==((====))== Unsloth 2025.8.5: Fast Gpt_Oss patching. Transformers: 4.56.0.dev0.\n", + " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.8.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.4.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = None. FA2 = False]\n", + " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n", + "Unsloth: Using float16 precision for gpt_oss won't work! Using float32.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "Eob7mSYFhQUm" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8c039ec5fb594077aa9947c2683ca1ef", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "\n", - "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n", - "\n", - "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n", - "\n", - "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n", - "\n", - "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", - "\n", - "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + "text/plain": [ + "model.safetensors.index.json: 0.00B [00:00, ?B/s]" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "FNQzEcDlhQUn" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "607d1555851348b7813f6a3db1844109", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "### Installation" + "text/plain": [ + "model-00001-of-00004.safetensors: 0%| | 0.00/4.00G [00:00=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n", - " \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n", - " \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n", - " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", - "!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n", - "!uv pip install --no-deps trl==0.22.2\n", - "!uv pip install git+https://github.com/gradio-app/trackio.git@more-env" + "text/plain": [ + "model-00002-of-00004.safetensors: 0%| | 0.00/4.00G [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", - " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", - " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", - " lora_alpha = 16,\n", - " lora_dropout = 0, # Supports any, but = 0 is optimized\n", - " bias = \"none\", # Supports any, but = \"none\" is optimized\n", - " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", - " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", - " random_state = 3407,\n", - " use_rslora = False, # We support rank stabilized LoRA\n", - " loftq_config = None, # And LoftQ\n", - ")" + "text/plain": [ + "tokenizer_config.json: 0.00B [00:00, ?B/s]" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "4-sFShVvnVgg" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "157cdd563d2145388b8288d7ed981f6f", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "### Reasoning Effort\n", - "The `gpt-oss` models from OpenAI include a feature that allows users to adjust the model's \"reasoning effort.\" This gives you control over the trade-off between the model's performance and its response speed (latency) which by the amount of token the model will use to think.\n", - "\n", - "----\n", - "\n", - "The `gpt-oss` models offer three distinct levels of reasoning effort you can choose from:\n", - "\n", - "* **Low**: Optimized for tasks that need very fast responses and don't require complex, multi-step reasoning.\n", - "* **Medium**: A balance between performance and speed.\n", - "* **High**: Provides the strongest reasoning performance for tasks that require it, though this results in higher latency." + "text/plain": [ + "tokenizer.json: 0%| | 0.00/27.9M [00:00system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: low\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>Equation: x^5 + 3x^4 - 10 = 3. So x^5 + 3x^4 - 13 =0. Solve for real roots? maybe numeric. Let's try approximate.\n", - "\n", - "We can test integer roots: try x=1 => 1+3\n" - ] - } - ], - "source": [ - "from transformers import TextStreamer\n", - "\n", - "messages = [\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", - "\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + "text/plain": [ + "special_tokens_map.json: 0%| | 0.00/446 [00:00system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: medium\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The user: \"Solve x^5 + 3x^4 - 10 = 3.\" Wait maybe it's an equation: x^5 + 3x^4 - 10 = 3. The variable x unknown. Solve for x. We need to solve the equation:\n", - "\n", - "x^\n" - ] - } - ], - "source": [ - "from transformers import TextStreamer\n", - "\n", - "messages = [\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", - "\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Unsloth: Making `model.base_model.model.model` require gradients\n" + ] + } + ], + "source": [ + "model = FastLanguageModel.get_peft_model(\n", + " model,\n", + " r = 8, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", + " lora_alpha = 16,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4-sFShVvnVgg" + }, + "source": [ + "### Reasoning Effort\n", + "The `gpt-oss` models from OpenAI include a feature that allows users to adjust the model's \"reasoning effort.\" This gives you control over the trade-off between the model's performance and its response speed (latency) which by the amount of token the model will use to think.\n", + "\n", + "----\n", + "\n", + "The `gpt-oss` models offer three distinct levels of reasoning effort you can choose from:\n", + "\n", + "* **Low**: Optimized for tasks that need very fast responses and don't require complex, multi-step reasoning.\n", + "* **Medium**: A balance between performance and speed.\n", + "* **High**: Provides the strongest reasoning performance for tasks that require it, though this results in higher latency." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "yxCi64FnnVgh", + "outputId": "26150958-7208-4dbd-ce07-bdac6748465b" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "M0iuyJt7nVgh" - }, - "source": [ - "Lastly we will test it using `reasoning_effort` to `high`" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: low\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>Equation: x^5 + 3x^4 - 10 = 3. So x^5 + 3x^4 - 13 =0. Solve for real roots? maybe numeric. Let's try approximate.\n", + "\n", + "We can test integer roots: try x=1 => 1+3\n" + ] + } + ], + "source": [ + "from transformers import TextStreamer\n", + "\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n", + ").to(\"cuda\")\n", + "\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IlAzq_RinVgh" + }, + "source": [ + "Changing the `reasoning_effort` to `medium` will make the model think longer. We have to increase the `max_new_tokens` to occupy the amount of the generated tokens but it will give better and more correct answer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "kaPPyXN1nVgh", + "outputId": "ff594b71-a82c-4203-fa6e-f9fd14b210a0" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QrjUXjN8nVgh", - "outputId": "9db0a3e3-5aae-40b6-8acb-a9b393d0d176" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: high\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation: x^5 + 3x^4 - 10 = 3. Or maybe it's x^5 + 3x^4 - 10 = 3? That seems like a polynomial equation: x^5 + 3x^4 - 10\n" - ] - } - ], - "source": [ - "from transformers import TextStreamer\n", - "\n", - "messages = [\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", - "\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: medium\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The user: \"Solve x^5 + 3x^4 - 10 = 3.\" Wait maybe it's an equation: x^5 + 3x^4 - 10 = 3. The variable x unknown. Solve for x. We need to solve the equation:\n", + "\n", + "x^\n" + ] + } + ], + "source": [ + "from transformers import TextStreamer\n", + "\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n", + ").to(\"cuda\")\n", + "\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M0iuyJt7nVgh" + }, + "source": [ + "Lastly we will test it using `reasoning_effort` to `high`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "QrjUXjN8nVgh", + "outputId": "9db0a3e3-5aae-40b6-8acb-a9b393d0d176" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "e6BnnYcbnVgh" - }, - "source": [ - "\n", - "### Data Prep" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: high\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation: x^5 + 3x^4 - 10 = 3. Or maybe it's x^5 + 3x^4 - 10 = 3? That seems like a polynomial equation: x^5 + 3x^4 - 10\n" + ] + } + ], + "source": [ + "from transformers import TextStreamer\n", + "\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n", + ").to(\"cuda\")\n", + "\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e6BnnYcbnVgh" + }, + "source": [ + "\n", + "### Data Prep" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "91gfk9L3nVgh" + }, + "source": [ + "The `HuggingFaceH4/Multilingual-Thinking` dataset will be utilized as our example. This dataset, available on Hugging Face, contains reasoning chain-of-thought examples derived from user questions that have been translated from English into four other languages. It is also the same dataset referenced in OpenAI's [cookbook](https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers) for fine-tuning. The purpose of using this dataset is to enable the model to learn and develop reasoning capabilities in these four distinct languages." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 183, + "referenced_widgets": [ + "8b8eb63337fb428fb0702ab599e2d402", + "ec16474c3bb2416ea72cda7801911a36", + "ba78a415e8b8469ea3ca3f4f5fe2d419", + "2a4965d875f640cf8a10998614308c10", + "b6bbb3fd3245428c9a56ccb007bdd1ab", + "f14e045ddcf54eef958e92c7a8616d50", + "8d0635071af84cf1ac18e9a052087e32", + "d1c64a303c6541f4a5463748383cecc1", + "470ed5fc391f4c8fbe4d4f07d5aa3e23", + "a9bd7392477840acbab43d9263955647", + "0017ec22a7504941934db02a385dce85", + "9a430ca8b86e4f279122b45267a038c0", + "e75b2c318d464bb8b4debc68621cb533", + "907f9b49253f46638f2c1ecc79116698", + "8e7481889c1d4d70bbf4f5b0dc849bdc", + "ad65c3013d2d4cedba1fd98ef835b3b5", + "e3a9a9b8868e40c3b754b4fb6a299906", + "29f0d621132742188596ce3a7dfb1704", + "737f0b3c8edd40c69ac7025c6ee00723", + "7e5c3cad61f9447dbfdc25e3487223b7", + "297f17e5d1e743c7acea1d15731d255e", + "30893988a2a4460696d92911a4ebede7", + "c13b432ba06341c09746c52307f866aa", + "1bce340c0f8848fe85db3beaf8dc1ed7", + "8f3aa28ce7c14c3a97629855721d0c25", + "62fafca550a7466fb478a161a1e5c541", + "4f93b270ee7b4eec95113b56214eada8", + "03a7eaea40cf4eb69b0f0d1e495e631c", + "3986d3adb14d48e1b5939e68f9d3ffc5", + "8cb4d60568bf4572a37870b8a1b510b2", + "97e57af4fdd84d8baeb52fea57b3ab14", + "602a471c56e54731a847d1b29f72e999", + "9518e8ada50747818ad94bf81118a964" + ] }, + "id": "62QfuPXBnVgi", + "outputId": "dfe615ff-591a-4a3d-fb3f-3198626cdd6b" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "91gfk9L3nVgh" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8b8eb63337fb428fb0702ab599e2d402", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "The `HuggingFaceH4/Multilingual-Thinking` dataset will be utilized as our example. This dataset, available on Hugging Face, contains reasoning chain-of-thought examples derived from user questions that have been translated from English into four other languages. It is also the same dataset referenced in OpenAI's [cookbook](https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers) for fine-tuning. The purpose of using this dataset is to enable the model to learn and develop reasoning capabilities in these four distinct languages." + "text/plain": [ + "README.md: 0.00B [00:00, ?B/s]" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 183, - "referenced_widgets": [ - "8b8eb63337fb428fb0702ab599e2d402", - "ec16474c3bb2416ea72cda7801911a36", - "ba78a415e8b8469ea3ca3f4f5fe2d419", - "2a4965d875f640cf8a10998614308c10", - "b6bbb3fd3245428c9a56ccb007bdd1ab", - "f14e045ddcf54eef958e92c7a8616d50", - "8d0635071af84cf1ac18e9a052087e32", - "d1c64a303c6541f4a5463748383cecc1", - "470ed5fc391f4c8fbe4d4f07d5aa3e23", - "a9bd7392477840acbab43d9263955647", - "0017ec22a7504941934db02a385dce85", - "9a430ca8b86e4f279122b45267a038c0", - "e75b2c318d464bb8b4debc68621cb533", - "907f9b49253f46638f2c1ecc79116698", - "8e7481889c1d4d70bbf4f5b0dc849bdc", - "ad65c3013d2d4cedba1fd98ef835b3b5", - "e3a9a9b8868e40c3b754b4fb6a299906", - "29f0d621132742188596ce3a7dfb1704", - "737f0b3c8edd40c69ac7025c6ee00723", - "7e5c3cad61f9447dbfdc25e3487223b7", - "297f17e5d1e743c7acea1d15731d255e", - "30893988a2a4460696d92911a4ebede7", - "c13b432ba06341c09746c52307f866aa", - "1bce340c0f8848fe85db3beaf8dc1ed7", - "8f3aa28ce7c14c3a97629855721d0c25", - "62fafca550a7466fb478a161a1e5c541", - "4f93b270ee7b4eec95113b56214eada8", - "03a7eaea40cf4eb69b0f0d1e495e631c", - "3986d3adb14d48e1b5939e68f9d3ffc5", - "8cb4d60568bf4572a37870b8a1b510b2", - "97e57af4fdd84d8baeb52fea57b3ab14", - "602a471c56e54731a847d1b29f72e999", - "9518e8ada50747818ad94bf81118a964" - ] - }, - "id": "62QfuPXBnVgi", - "outputId": "dfe615ff-591a-4a3d-fb3f-3198626cdd6b" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9a430ca8b86e4f279122b45267a038c0", + "version_major": 2, + "version_minor": 0 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8b8eb63337fb428fb0702ab599e2d402", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "README.md: 0.00B [00:00, ?B/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9a430ca8b86e4f279122b45267a038c0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "data/train-00000-of-00001.parquet: 0%| | 0.00/5.29M [00:00system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: medium\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", - "\n", - "reasoning language: French\n", - "\n", - "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.\n", - "\n", - "Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", - "\n", - "1. **Open the \"Trending\" tab** on the Twitter app or website – it updates constantly! \n", - "2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up. \n", - "3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads). \n", - "4. **Check regional trends** – they often differ by location! \n", - "\n", - "Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the \"Distracted Boyfriend\" meme revival), celebrity drama, or unexpected events (hello, weather disasters!). \n", - "\n", - "Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>\n" - ] - } - ], - "source": [ - "print(dataset[0]['text'])" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: medium\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", + "\n", + "reasoning language: French\n", + "\n", + "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.\n", + "\n", + "Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", + "\n", + "1. **Open the \"Trending\" tab** on the Twitter app or website – it updates constantly! \n", + "2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up. \n", + "3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads). \n", + "4. **Check regional trends** – they often differ by location! \n", + "\n", + "Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the \"Distracted Boyfriend\" meme revival), celebrity drama, or unexpected events (hello, weather disasters!). \n", + "\n", + "Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>\n" + ] + } + ], + "source": [ + "print(dataset[0]['text'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tQ3i-AMFnVgj" + }, + "source": [ + "What is unique about GPT-OSS is that it uses OpenAI [Harmony](https://github.com/openai/harmony) format which support conversation structures, reasoning output, and tool calling." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rtdsxyl6nVgk" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We set some environment variables to customize the Trackio dashboard for experiment tracking\n", + "import os\n", + "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"\n", + "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"\n", + "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "0fc33d9d7b2e486ea16c7e9655d1f078", + "c4568dd761a140b6bb9d5996a98a22d4", + "587e44e5af14403582c0b87ef85813b4", + "1adeb75bbdaa4ef388c82f786916509a", + "1cce8185eab94b189fee6a7efb0eb3dc", + "4362a20e703c42d4b0b92dc410d62889", + "227eab802b6543d8b6915da6fed18c6e", + "bbd94cb3957e4b0b9fde5ef117753d43", + "17ee69f3ffdd4985b436803c99a80b3d", + "5df9512f00d842d5bba5da9f97d703ac", + "aa886d9ac13d40c2a90625943b782168" + ] }, + "id": "O-XZLeLYnVgk", + "outputId": "1ffe6822-e7a2-4c69-c764-59933ef359ca" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "tQ3i-AMFnVgj" - }, - "source": [ - "What is unique about GPT-OSS is that it uses OpenAI [Harmony](https://github.com/openai/harmony) format which support conversation structures, reasoning output, and tool calling." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Unsloth: Switching to float32 training since model cannot work with float16\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "Rtdsxyl6nVgk" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0fc33d9d7b2e486ea16c7e9655d1f078", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "\n", - "### Train the model\n", - "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`." + "text/plain": [ + "Unsloth: Tokenizing [\"text\"] (num_proc=2): 0%| | 0/1000 [00:00user<|message|>\", response_part=\"<|start|>assistant<|channel|>final<|message|>\")\n", + "\n", + "trainer = train_on_responses_only(\n", + " trainer,\n", + " **gpt_oss_kwargs,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's verify masking the instruction part is done! Let's print the 100th row again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's print the masked out example - you should see only the answer is present:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100][\"labels\"]]).replace(tokenizer.pad_token, \" \")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 67, - "referenced_widgets": [ - "0fc33d9d7b2e486ea16c7e9655d1f078", - "c4568dd761a140b6bb9d5996a98a22d4", - "587e44e5af14403582c0b87ef85813b4", - "1adeb75bbdaa4ef388c82f786916509a", - "1cce8185eab94b189fee6a7efb0eb3dc", - "4362a20e703c42d4b0b92dc410d62889", - "227eab802b6543d8b6915da6fed18c6e", - "bbd94cb3957e4b0b9fde5ef117753d43", - "17ee69f3ffdd4985b436803c99a80b3d", - "5df9512f00d842d5bba5da9f97d703ac", - "aa886d9ac13d40c2a90625943b782168" - ] - }, - "id": "O-XZLeLYnVgk", - "outputId": "1ffe6822-e7a2-4c69-c764-59933ef359ca" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unsloth: Switching to float32 training since model cannot work with float16\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0fc33d9d7b2e486ea16c7e9655d1f078", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Unsloth: Tokenizing [\"text\"] (num_proc=2): 0%| | 0/1000 [00:00user<|message|>\", response_part=\"<|start|>assistant<|channel|>final<|message|>\")\n", - "\n", - "trainer = train_on_responses_only(\n", - " trainer,\n", - " **gpt_oss_kwargs,\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Unsloth: Will smartly offload gradients to save VRAM!\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "Yu3FQpEWhQUq" - }, - "source": [ - "Let's verify masking the instruction part is done! Let's print the 100th row again." + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " \n", + " [30/30 08:34, Epoch 0/1]\n", + "
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StepTraining Loss
12.130500
22.918100
32.419300
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51.978200
62.119900
71.825800
81.703400
91.974400
101.796700
111.698900
121.637100
131.633600
141.570100
151.418700
161.643800
171.697200
181.830000
191.386500
201.400800
211.329000
221.382800
231.504600
241.589200
251.400000
261.431400
271.465200
281.468800
291.421100
301.408200

" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "_G3eBV3EnVgk", + "outputId": "7c86ff1e-b5b5-47f6-bbc4-eec30a219e46" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "G86TKxL0hQUq" - }, - "outputs": [], - "source": [ - "tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "645.6936 seconds used for training.\n", + "10.76 minutes used for training.\n", + "Peak reserved memory = 12.975 GB.\n", + "Peak reserved memory for training = 0.164 GB.\n", + "Peak reserved memory % of max memory = 88.02 %.\n", + "Peak reserved memory for training % of max memory = 1.113 %.\n" + ] + } + ], + "source": [ + "# @title Show final memory and time stats\n", + "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", + "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", + "used_percentage = round(used_memory / max_memory * 100, 3)\n", + "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", + "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", + "print(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\n", + "print(f\"Peak reserved memory = {used_memory} GB.\")\n", + "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", + "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", + "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CuK0hVOsnVgk" + }, + "source": [ + "\n", + "### Inference\n", + "Let's run the model! You can change the instruction and input - leave the output blank!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "RdVCmTuBnVgl", + "outputId": "266de72f-20d3-4253-fffe-6b2764a5a7d9" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "hH2_7aPShQUq" - }, - "source": [ - "Now let's print the masked out example - you should see only the answer is present:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: medium\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", + "\n", + "reasoning language: French\n", + "\n", + "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The equation is \\(x^5 + 3x^4 - 10 = 3\\), or \\(x^5 + 3x^4 - 13 = 0\\). So we need to find the roots of \\(x^5 + 3x^4 - 13\n" + ] + } + ], + "source": [ + "messages = [\n", + " {\"role\": \"system\", \"content\": \"reasoning language: French\\n\\nYou are a helpful assistant that can solve mathematical problems.\"},\n", + " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"medium\",\n", + ").to(\"cuda\")\n", + "from transformers import TextStreamer\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5e1j8KRb4AwO" + }, + "source": [ + "\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** Currently finetunes can only be loaded via Unsloth in the meantime - we're working on vLLM and GGUF exporting!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "Ds7ByU7e4KF7" + }, + "outputs": [], + "source": [ + "model.save_pretrained(\"finetuned_model\")\n", + "# model.push_to_hub(\"hf_username/finetuned_model\", token = \"hf_...\") # Save to HF" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ELyXzRpl4hr0" + }, + "source": [ + "To run the finetuned model, you can do the below after setting `if False` to `if True` in a new instance." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "kCMDSxvD4SKu", + "outputId": "dbf449e3-d794-490c-dc1f-a2b9afdb93ef" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "UEr_st9zhQUq" - }, - "outputs": [], - "source": [ - "tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100][\"labels\"]]).replace(tokenizer.pad_token, \" \")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", + "Knowledge cutoff: 2024-06\n", + "Current date: 2025-08-13\n", + "\n", + "Reasoning: high\n", + "\n", + "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", + "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", + "\n", + "reasoning language: French\n", + "\n", + "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 → x^5 + 3x^\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"finetuned_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = 1024,\n", + " dtype = None,\n", + " load_in_4bit = True,\n", + " )\n", + "\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": \"reasoning language: French\\n\\nYou are a helpful assistant that can solve mathematical problems.\"},\n", + " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", + "]\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + " return_dict = True,\n", + " reasoning_effort = \"high\",\n", + ").to(\"cuda\")\n", + "from transformers import TextStreamer\n", + "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving to float16 for VLLM or mxfp4\n", + "\n", + "We also support saving to `float16` or `mxfp4` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge and push to hub in mxfp4 4bit format\n", + "if False:\n", + " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"mxfp4\")\n", + "if False: model.push_to_hub_merged(\"repo_id/repo_name\", tokenizer, token = \"hf...\", save_method = \"mxfp4\")\n", + "\n", + "# Merge and push to hub in 16bit\n", + "if False:\n", + " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"merged_16bit\")\n", + "if False: # Pushing to HF Hub\n", + " model.push_to_hub_merged(\"hf/gpt-oss-finetune\", tokenizer, save_method = \"merged_16bit\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qMNviX7XnVgl" + }, + "source": [ + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", + "
\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.13.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0017ec22a7504941934db02a385dce85": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "AaNK0XqvhQUq", - "outputId": "3b2ebc4d-fd49-4023-e884-175678a227df" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GPU = Tesla T4. 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"@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7322a242ad4744168de44963be435725", + "IPY_MODEL_30fd12adf3a14aad813b0d9b29670596", + "IPY_MODEL_3a1670c82c4544578816944852a3a48f" ], - "source": [ - "# @title Show current memory stats\n", - "gpu_stats = torch.cuda.get_device_properties(0)\n", - "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", - "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", - "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", - "print(f\"{start_gpu_memory} GB of memory reserved.\")" - ] + "layout": "IPY_MODEL_fd443c983f1a409aa6be506aea521e9a" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "W5VwHZCshQUq" - }, - "source": [ - "Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`" - ] + "09d35ff962e24e0791932a5d60a8a911": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "aFaejiSonVgk", - "outputId": "f9768c59-df45-4b80-b150-2e99036837ae" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", - "The tokenizer has new special tokens that are also defined in the model configs. The model configs were aligned accordingly. Updated tokens: {'bos_token_id': 199998, 'pad_token_id': 200017}\n", - "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", - " \\\\ /| Num examples = 1,000 | Num Epochs = 1 | Total steps = 30\n", - "O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n", - "\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n", - " \"-____-\" Trainable parameters = 3,981,312 of 20,918,738,496 (0.02% trained)\n", - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unsloth: Will smartly offload gradients to save VRAM!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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StepTraining Loss
12.130500
22.918100
32.419300
42.167900
51.978200
62.119900
71.825800
81.703400
91.974400
101.796700
111.698900
121.637100
131.633600
141.570100
151.418700
161.643800
171.697200
181.830000
191.386500
201.400800
211.329000
221.382800
231.504600
241.589200
251.400000
261.431400
271.465200
281.468800
291.421100
301.408200

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"source": [ - "trainer_stats = trainer.train()" - ] + "layout": "IPY_MODEL_1cce8185eab94b189fee6a7efb0eb3dc" + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_G3eBV3EnVgk", - "outputId": "7c86ff1e-b5b5-47f6-bbc4-eec30a219e46" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "645.6936 seconds used for training.\n", - "10.76 minutes used for training.\n", - "Peak reserved memory = 12.975 GB.\n", - "Peak reserved memory for training = 0.164 GB.\n", - "Peak reserved memory % of max memory = 88.02 %.\n", - "Peak reserved memory for training % of max memory = 1.113 %.\n" - ] - } + "11ada4258a894a27a4e096257ecac8ff": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1447cc59ce834e9b950c9f78d557f11c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_b1cdcb9c0b9a463bbbc4a16b64f24e12", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_0c6c7e5a315e44c0a545515626ef3606", + "value": 1 + } + }, + "14dd75fc40d94565b05931f6d9519b8a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6378d55aada8467688da8d1da0c123ce", + "IPY_MODEL_ff74e51179ab471b898e11008c91629e", + "IPY_MODEL_3821f16f51ab4f3ebedb06c94d3846ce" ], - "source": [ - "# @title Show final memory and time stats\n", - "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", - "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", - "used_percentage = round(used_memory / max_memory * 100, 3)\n", - "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", - "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", - "print(\n", - " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", - ")\n", - "print(f\"Peak reserved memory = {used_memory} GB.\")\n", - "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", - "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", - "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" - ] + "layout": "IPY_MODEL_1f050ac26f114a36b2c8fbf810084bf5" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "CuK0hVOsnVgk" - }, - "source": [ - "\n", - "### Inference\n", - "Let's run the model! You can change the instruction and input - leave the output blank!" - ] + "157cdd563d2145388b8288d7ed981f6f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d302c13ddea44894bad6494309771580", + "IPY_MODEL_d307e2839dae4480b07e25b1db2ff9e1", + "IPY_MODEL_8cd9481d509d40d398acda0fe597c999" + ], + "layout": "IPY_MODEL_bb816edcb65640688306f1b099a1a088" + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RdVCmTuBnVgl", - "outputId": "266de72f-20d3-4253-fffe-6b2764a5a7d9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n", - "Knowledge cutoff: 2024-06\n", - "Current date: 2025-08-13\n", - "\n", - "Reasoning: medium\n", - "\n", - "# Valid channels: analysis, commentary, final. Channel must be included for every message.\n", - "Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions\n", - "\n", - "reasoning language: French\n", - "\n", - "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The equation is \\(x^5 + 3x^4 - 10 = 3\\), or \\(x^5 + 3x^4 - 13 = 0\\). So we need to find the roots of \\(x^5 + 3x^4 - 13\n" - ] - } + "17ee69f3ffdd4985b436803c99a80b3d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "18524360ea164f8794178e7dd4ece59c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + 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"IPY_MODEL_ba25ca3bc967493c8d9f53670d6245b9" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "5e1j8KRb4AwO" - }, - "source": [ - "\n", - "### Saving, loading finetuned models\n", - "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", - "\n", - "**[NOTE]** Currently finetunes can only be loaded via Unsloth in the meantime - we're working on vLLM and GGUF exporting!" - ] + "297f17e5d1e743c7acea1d15731d255e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": 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"model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_444905088dc045faa382e6fdec70574a", + "IPY_MODEL_65f08647736f42c285980f4580b8c3f2", + "IPY_MODEL_ddd55b7ba1164e809f9406bf2f9de9a4" ], - "source": [ - "if False:\n", - " from unsloth import FastLanguageModel\n", - " model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"finetuned_model\", # YOUR MODEL YOU USED FOR TRAINING\n", - " max_seq_length = 1024,\n", - " dtype = None,\n", - " load_in_4bit = True,\n", - " )\n", - "\n", - "messages = [\n", - " {\"role\": \"system\", \"content\": \"reasoning language: French\\n\\nYou are a helpful assistant that can solve mathematical problems.\"},\n", - " {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n", - "]\n", - "inputs = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt = True,\n", - " return_tensors = \"pt\",\n", - " return_dict = True,\n", - " reasoning_effort = \"high\",\n", - ").to(\"cuda\")\n", - "from transformers import TextStreamer\n", - "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" - ] + "layout": "IPY_MODEL_09d35ff962e24e0791932a5d60a8a911" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "y5u-_HjqhQU3" - }, - "source": [ - "### Saving to float16 for VLLM or mxfp4\n", - "\n", - "We also support saving to `float16` or `mxfp4` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." - ] + "33296d2012e3437dac6393b1e447d89a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ad39a8481898489b858c2e797faa564a", + "max": 1000, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_322de8a1e48a4c7bbe033561f12191de", + "value": 1000 + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "HHEXm8jlhQU3" - }, - "outputs": [], - "source": [ - "# Merge and push to hub in mxfp4 4bit format\n", - "if False:\n", - " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"mxfp4\")\n", - "if False: model.push_to_hub_merged(\"repo_id/repo_name\", tokenizer, token = \"hf...\", save_method = \"mxfp4\")\n", - "\n", - "# Merge and push to hub in 16bit\n", - "if False:\n", - " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"merged_16bit\")\n", - "if False: # Pushing to HF Hub\n", - " model.push_to_hub_merged(\"hf/gpt-oss-finetune\", tokenizer, save_method = \"merged_16bit\", token = \"\")" - ] + "332a4aedcef1459b8a553a9c8a27a72d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "qMNviX7XnVgl" - }, - "source": [ - "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", - "\n", - "Some other links:\n", - "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n", - "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", - "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", - "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", - "\n", - "

\n", - " \n", - " \n", - " \n", - "\n", - " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", - "
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00:00:00 2001 From: Abubakar Abid Date: Tue, 14 Oct 2025 14:02:25 -0700 Subject: [PATCH 12/19] revert --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 232 +++++++++++++---------------- 1 file changed, 107 insertions(+), 125 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 8db64ddb..35598701 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -2,13 +2,15 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "yzrOIcNbnVgY" + }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", "\n", "\n", - " Join Discord if you need help + ⭐ Star us on Github ⭐\n", + " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", @@ -18,21 +20,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "ZppM2UflnVgb" + }, "source": [ "### News" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "a5mojalInVgc" + }, "source": [ "\n", - "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n", + "[Vision RL](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) is now supported! Train Qwen2.5-VL, Gemma 3 etc. with GSPO or GRPO.\n", "\n", - "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n", + "Introducing Unsloth [Standby for RL](https://docs.unsloth.ai/basics/memory-efficient-rl): GRPO is now faster, uses 30% less memory with 2x longer context.\n", "\n", - "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n", + "Gpt-oss fine-tuning now supports 8\u00d7 longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training)\n", "\n", "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", "\n", @@ -41,7 +47,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "C3wk7M5nnVgc" + }, "source": [ "### Installation" ] @@ -49,22 +57,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "dqkFWxkVnVgc" + }, "outputs": [], - "source": [ - "%%capture\n", - "!pip install --upgrade -qqq uv\n", - "try: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\n", - "except: get_numpy = \"numpy\"\n", - "!uv pip install -qqq \\\n", - " \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n", - " \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n", - " \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n", - " git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n", - "!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n", - "!uv pip install --no-deps trl==0.22.2\n", - "!uv pip install git+https://github.com/gradio-app/trackio.git@more-env" - ] + "source": "%%capture\n# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2" }, { "cell_type": "markdown", @@ -223,8 +220,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "🦥 Unsloth Zoo will now patch everything to make training faster!\n", + "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2025.8.5: Fast Gpt_Oss patching. Transformers: 4.56.0.dev0.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.8.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.4.0\n", @@ -516,7 +513,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", + ").to(model.device)\n", "\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -570,7 +567,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", + ").to(model.device)\n", "\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -622,7 +619,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(\"cuda\")\n", + ").to(model.device)\n", "\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -851,18 +848,18 @@ "\n", "reasoning language: French\n", "\n", - "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.\n", + "You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus r\u00e9centes. Tout d'abord, je dois v\u00e9rifier si j'ai acc\u00e8s \u00e0 des donn\u00e9es en temps r\u00e9el. \u00c9tant donn\u00e9 que je ne peux pas naviguer sur Internet ou acc\u00e9der directement \u00e0 l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils g\u00e9n\u00e9raux sur la fa\u00e7on de les trouver.\n", "\n", - "Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", + "Je devrais pr\u00e9ciser que les tendances Twitter \u00e9voluent rapidement et sont sp\u00e9cifiques \u00e0 chaque r\u00e9gion. Je pourrais sugg\u00e9rer de consulter la section \u00ab\u00a0En vogue\u00a0\u00bb sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient \u00eatre utiles. Il est important de souligner que les tendances varient selon la r\u00e9gion et l'heure de la journ\u00e9e. Je devrais garder un ton amical et bienveillant, peut-\u00eatre ajouter un emoji pour rester l\u00e9ger. Je vais structurer ma r\u00e9ponse \u00e9tape par \u00e9tape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des donn\u00e9es en temps r\u00e9el et proposer d'autres m\u00e9thodes. Je conserverai un langage simple et convivial, en \u00e9vitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there! While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:\n", "\n", - "1. **Open the \"Trending\" tab** on the Twitter app or website – it updates constantly! \n", - "2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up. \n", + "1. **Open the \"Trending\" tab** on the Twitter app or website \u2013 it updates constantly! \n", + "2. **Search for hashtags** like #Trending or #Viral to see what\u2019s blowing up. \n", "3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads). \n", - "4. **Check regional trends** – they often differ by location! \n", + "4. **Check regional trends** \u2013 they often differ by location! \n", "\n", "Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the \"Distracted Boyfriend\" meme revival), celebrity drama, or unexpected events (hello, weather disasters!). \n", "\n", - "Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>\n" + "Want me to brainstorm *what* might trend next? I\u2019ve got ideas!<|return|>\n" ] } ], @@ -892,20 +889,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We set some environment variables to customize the Trackio dashboard for experiment tracking\n", - "import os\n", - "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"\n", - "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"\n", - "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -969,7 +953,7 @@ " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", - " report_to = \"trackio\",\n", + " report_to = \"none\", # Use this for WandB etc\n", " ),\n", ")" ] @@ -1337,7 +1321,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"medium\",\n", - ").to(\"cuda\")\n", + ").to(model.device)\n", "from transformers import TextStreamer\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -1402,7 +1386,7 @@ "\n", "reasoning language: French\n", "\n", - "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 → x^5 + 3x^\n" + "You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 \u2192 x^5 + 3x^\n" ] } ], @@ -1426,7 +1410,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"high\",\n", - ").to(\"cuda\")\n", + ").to(model.device)\n", "from transformers import TextStreamer\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -1448,12 +1432,12 @@ "source": [ "# Merge and push to hub in mxfp4 4bit format\n", "if False:\n", - " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"mxfp4\")\n", - "if False: model.push_to_hub_merged(\"repo_id/repo_name\", tokenizer, token = \"hf...\", save_method = \"mxfp4\")\n", + " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method=\"mxfp4\")\n", + "if False: model.push_to_hub_merged(\"repo_id/repo_name\", tokenizer, token=\"hf...\", save_method=\"mxfp4\")\n", "\n", "# Merge and push to hub in 16bit\n", "if False:\n", - " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"merged_16bit\")\n", + " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method=\"merged_16bit\")\n", "if False: # Pushing to HF Hub\n", " model.push_to_hub_merged(\"hf/gpt-oss-finetune\", tokenizer, save_method = \"merged_16bit\", token = \"\")" ] @@ -1477,7 +1461,7 @@ " \n", " \n", "\n", - " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", + " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", "\n" ] } @@ -1489,13 +1473,11 @@ "provenance": [] }, "kernelspec": { - "display_name": ".venv", - "language": "python", + "display_name": "Python 3", "name": "python3" }, "language_info": { - "name": "python", - "version": "3.13.7" + "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { @@ -1807,9 +1789,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_83dd0a7d75d544f1a64fb265822b1dc6", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_28b1a6aef393405ba325d29e470b9332", - "value": " 22.8k/? [00:00<00:00, 1.88MB/s]" + "value": "\u200722.8k/?\u2007[00:00<00:00,\u20071.88MB/s]" } }, "0fc33d9d7b2e486ea16c7e9655d1f078": { @@ -2010,9 +1992,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_98122f1f5c974405aec8cee21d511235", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_60ee8e94b3794c6085a03a96058d03ee", - "value": " 4.00G/4.00G [00:47<00:00, 171MB/s]" + "value": "\u20074.00G/4.00G\u2007[00:47<00:00,\u2007171MB/s]" } }, "19983e4ce30944c7a57abfe01e463eb0": { @@ -2031,9 +2013,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9643968ed03642429372c2dac797031b", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_48bb950cb7224cf681b8892d9bae389d", - "value": " 4.00G/4.00G [00:56<00:00, 25.5MB/s]" + "value": "\u20074.00G/4.00G\u2007[00:56<00:00,\u200725.5MB/s]" } }, "1adeb75bbdaa4ef388c82f786916509a": { @@ -2052,9 +2034,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5df9512f00d842d5bba5da9f97d703ac", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_aa886d9ac13d40c2a90625943b782168", - "value": " 1000/1000 [00:08<00:00, 142.30 examples/s]" + "value": "\u20071000/1000\u2007[00:08<00:00,\u2007142.30\u2007examples/s]" } }, "1b7009babefe4108be77c969c97c6c56": { @@ -2089,9 +2071,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_03a7eaea40cf4eb69b0f0d1e495e631c", - 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"placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_04b1a6ba8ec54e6d8ff2f9406d0e708f", - "value": "Map: 100%" + "value": "Map:\u2007100%" } }, "5d6c9f818ec94c5d9f8b325839371963": { @@ -3767,9 +3749,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_602a471c56e54731a847d1b29f72e999", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_9518e8ada50747818ad94bf81118a964", - "value": " 1000/1000 [00:00<00:00, 2996.56 examples/s]" + "value": "\u20071000/1000\u2007[00:00<00:00,\u20072996.56\u2007examples/s]" } }, "6378d55aada8467688da8d1da0c123ce": { @@ -3788,9 +3770,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_99dfd860e52240838e9c55238884fcee", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_a17f3673fb6c4971bd53489a80c12b03", - "value": "generation_config.json: 100%" + "value": "generation_config.json:\u2007100%" } }, "65d2db12df6942b98bda16b738191f34": { @@ -3885,9 +3867,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2e560b107cbf4f9ea1b34bf3a3094678", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_74ebde2ac07d49f0ba65b7d70cea09f1", - "value": " 1.16G/1.16G [00:19<00:00, 51.4MB/s]" + "value": "\u20071.16G/1.16G\u2007[00:19<00:00,\u200751.4MB/s]" } }, "69176a4379e74670a765be4b916e718a": { @@ -3906,9 +3888,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5f96703d9fd64ee7b52b02662e7afffc", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_51c530ca4981460c99501f5f90f3a182", - "value": "model-00003-of-00004.safetensors: 100%" + "value": "model-00003-of-00004.safetensors:\u2007100%" } }, "6c279fe5cb444673a65f1caba4648fc4": { @@ -4128,9 +4110,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b372d6ed1c204203be1fac53f2093c62", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_376cd15963c84026a4ba2a2c212b813e", - "value": "model.safetensors.index.json: " + "value": "model.safetensors.index.json:\u2007" } }, "7322a242ad4744168de44963be435725": { @@ -4149,9 +4131,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_88e50815be2a48e2a434b78ea4b98bd2", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_5e42a9d44ffe44eebf95d3bc0fd0f752", - "value": "special_tokens_map.json: 100%" + "value": "special_tokens_map.json:\u2007100%" } }, "737f0b3c8edd40c69ac7025c6ee00723": { @@ -4253,9 +4235,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_6d1644394190402baf9a58b00b1b3de8", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_3c88be2e8d5b4559b7c1928e7a46e847", - "value": " 15.1k/? [00:00<00:00, 901kB/s]" + "value": "\u200715.1k/?\u2007[00:00<00:00,\u2007901kB/s]" } }, "7e5c3cad61f9447dbfdc25e3487223b7": { @@ -4566,9 +4548,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_040250e6afb74feeb107c69e50a985bc", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_5d6c9f818ec94c5d9f8b325839371963", - "value": " 27.9M/27.9M [00:00<00:00, 42.9MB/s]" + "value": "\u200727.9M/27.9M\u2007[00:00<00:00,\u200742.9MB/s]" } }, "8d0635071af84cf1ac18e9a052087e32": { @@ -4654,9 +4636,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_297f17e5d1e743c7acea1d15731d255e", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_30893988a2a4460696d92911a4ebede7", - "value": " 5.29M/5.29M [00:00<00:00, 8.80MB/s]" + "value": "\u20075.29M/5.29M\u2007[00:00<00:00,\u20078.80MB/s]" } }, "8ec51fbe49f74f82b0f13c658f5d6bf8": { @@ -4948,9 +4930,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8f39efb61c224ae18db657ce38efd085", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_2a2612b9d72c49089ebb79bb28c0c415", - "value": " 1.19M/? [00:00<00:00, 60.5MB/s]" + "value": "\u20071.19M/?\u2007[00:00<00:00,\u200760.5MB/s]" } }, "99dfd860e52240838e9c55238884fcee": { @@ -5999,9 +5981,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4362a20e703c42d4b0b92dc410d62889", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_227eab802b6543d8b6915da6fed18c6e", - "value": "Unsloth: Tokenizing ["text"] (num_proc=2): 100%" + "value": "Unsloth:\u2007Tokenizing\u2007["text"]\u2007(num_proc=2):\u2007100%" } }, "c4e07ba599fc462792e39b6f3841ec46": { @@ -6020,9 +6002,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f1237a5c19014663b8ec6475ff81091d", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_585b94dcbd1c4a1595c7c6b110ead7ef", - "value": "model-00001-of-00004.safetensors: 100%" + "value": "model-00001-of-00004.safetensors:\u2007100%" } }, "c5996543c5c346a99000c70e810f8e8c": { @@ -6041,9 +6023,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_bd365bd853fd417aa7b7096ea1e9540c", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_fd15ab7222824c9abcce3a17cc0209af", - "value": "model-00004-of-00004.safetensors: 100%" + "value": "model-00004-of-00004.safetensors:\u2007100%" } }, "cb7de23470ce4dbbbb3a636d1aa0af9c": { @@ -6221,9 +6203,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c0615e2ed6c246d3bd64e50002f1b5cf", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_72986da11c5c400b8f3fcf73cebf8af8", - "value": "tokenizer.json: 100%" + "value": "tokenizer.json:\u2007100%" } }, "d307e2839dae4480b07e25b1db2ff9e1": { @@ -6266,9 +6248,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_add72aaf688a4ad8bfe7b5ffda08d21d", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_70f86aee84a143159feded54e0b0e2ee", - "value": "tokenizer_config.json: " + "value": "tokenizer_config.json:\u2007" } }, "d827c81f690044e2b3002e81be8ccc86": { @@ -6287,9 +6269,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ebb49ff5feff47aca6953a77806bfcc0", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_f3c6916566f0483082b75a6232501001", - "value": "model-00002-of-00004.safetensors: 100%" + "value": "model-00002-of-00004.safetensors:\u2007100%" } }, "d9b1cfdaa58f4a579addc1bfb41e3622": { @@ -6382,9 +6364,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8d925b65a79240f0bad9cd8add2bfec7", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_cb7de23470ce4dbbbb3a636d1aa0af9c", - "value": " 4/4 [01:00<00:00, 12.86s/it]" + "value": "\u20074/4\u2007[01:00<00:00,\u200712.86s/it]" } }, "ded74fd1bf114fe1a7c3d1bc0b6dd6ab": { @@ -6507,9 +6489,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_11ada4258a894a27a4e096257ecac8ff", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_f527df8dc8734cbcac2bfe27faaa7dfa", - "value": "chat_template.jinja: " + "value": "chat_template.jinja:\u2007" } }, "e75b2c318d464bb8b4debc68621cb533": { @@ -6528,9 +6510,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e3a9a9b8868e40c3b754b4fb6a299906", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_29f0d621132742188596ce3a7dfb1704", - "value": "data/train-00000-of-00001.parquet: 100%" + "value": "data/train-00000-of-00001.parquet:\u2007100%" } }, "ebb49ff5feff47aca6953a77806bfcc0": { @@ -6601,9 +6583,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f14e045ddcf54eef958e92c7a8616d50", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_8d0635071af84cf1ac18e9a052087e32", - "value": "README.md: " + "value": "README.md:\u2007" } }, "ecb9b5a306cc4244a12f8bdd7c65e498": { @@ -6942,9 +6924,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_04bc14d9112242259867abad6efc53c3", - "placeholder": "​", + "placeholder": "\u200b", "style": "IPY_MODEL_2e9287b93e93412b9f2b12cd98d69ab6", - "value": " 1000/1000 [00:00<00:00, 1151.76 examples/s]" + "value": "\u20071000/1000\u2007[00:00<00:00,\u20071151.76\u2007examples/s]" } }, "ff74e51179ab471b898e11008c91629e": { @@ -6977,4 +6959,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 90d7486bf59cffd89e6793ddc9f45179a8dad7b3 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Tue, 14 Oct 2025 14:03:15 -0700 Subject: [PATCH 13/19] changes --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 50 +++++++++++++----------------- 1 file changed, 21 insertions(+), 29 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 35598701..acb1d922 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -2,9 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "id": "yzrOIcNbnVgY" - }, + "metadata": {}, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", @@ -20,25 +18,21 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "ZppM2UflnVgb" - }, + "metadata": {}, "source": [ "### News" ] }, { "cell_type": "markdown", - "metadata": { - "id": "a5mojalInVgc" - }, + "metadata": {}, "source": [ "\n", - "[Vision RL](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) is now supported! Train Qwen2.5-VL, Gemma 3 etc. with GSPO or GRPO.\n", + "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n", "\n", - "Introducing Unsloth [Standby for RL](https://docs.unsloth.ai/basics/memory-efficient-rl): GRPO is now faster, uses 30% less memory with 2x longer context.\n", + "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n", "\n", - "Gpt-oss fine-tuning now supports 8\u00d7 longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training)\n", + "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n", "\n", "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", "\n", @@ -47,9 +41,7 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "C3wk7M5nnVgc" - }, + "metadata": {}, "source": [ "### Installation" ] @@ -57,11 +49,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "dqkFWxkVnVgc" - }, + "metadata": {}, "outputs": [], - "source": "%%capture\n# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2" + "source": "%%capture\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2" }, { "cell_type": "markdown", @@ -513,7 +503,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(model.device)\n", + ").to(\"cuda\")\n", "\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -567,7 +557,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(model.device)\n", + ").to(\"cuda\")\n", "\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -619,7 +609,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n", - ").to(model.device)\n", + ").to(\"cuda\")\n", "\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -1321,7 +1311,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"medium\",\n", - ").to(model.device)\n", + ").to(\"cuda\")\n", "from transformers import TextStreamer\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -1410,7 +1400,7 @@ " return_tensors = \"pt\",\n", " return_dict = True,\n", " reasoning_effort = \"high\",\n", - ").to(model.device)\n", + ").to(\"cuda\")\n", "from transformers import TextStreamer\n", "_ = model.generate(**inputs, max_new_tokens = 64, streamer = TextStreamer(tokenizer))" ] @@ -1432,12 +1422,12 @@ "source": [ "# Merge and push to hub in mxfp4 4bit format\n", "if False:\n", - " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method=\"mxfp4\")\n", - "if False: model.push_to_hub_merged(\"repo_id/repo_name\", tokenizer, token=\"hf...\", save_method=\"mxfp4\")\n", + " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"mxfp4\")\n", + "if False: model.push_to_hub_merged(\"repo_id/repo_name\", tokenizer, token = \"hf...\", save_method = \"mxfp4\")\n", "\n", "# Merge and push to hub in 16bit\n", "if False:\n", - " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method=\"merged_16bit\")\n", + " model.save_pretrained_merged(\"finetuned_model\", tokenizer, save_method = \"merged_16bit\")\n", "if False: # Pushing to HF Hub\n", " model.push_to_hub_merged(\"hf/gpt-oss-finetune\", tokenizer, save_method = \"merged_16bit\", token = \"\")" ] @@ -1473,11 +1463,13 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", + "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "name": "python", + "version": "3.13.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { From 3fab31137793e8461ab5f312e41d739ecd3d14e4 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Tue, 14 Oct 2025 14:09:42 -0700 Subject: [PATCH 14/19] Update package installation commands in notebook Added installation of trackio package. --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index acb1d922..685ec235 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -51,7 +51,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "%%capture\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2" + "source": "%%capture\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2\n!uv pip install trackio<=1.0" }, { "cell_type": "markdown", @@ -6951,4 +6951,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 7d535cd18ddac4477963e1b2b0eedbdbf9174b61 Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Tue, 14 Oct 2025 14:11:34 -0700 Subject: [PATCH 15/19] Customize Trackio dashboard with environment variables Added environment variable settings for Trackio dashboard customization. --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 685ec235..374c5487 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -926,6 +926,12 @@ ], "source": [ "from trl import SFTConfig, SFTTrainer\n", + "import os\n", + "\n", + "# Set some environment variables to customize the Trackio dashboard for experiment tracking\n", + "os.environ["TRACKIO_LOGO_LIGHT_URL"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"", + "os.environ["TRACKIO_LOGO_DARK_URL"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"", + "os.environ["TRACKIO_PLOT_ORDER"] = \"train/loss\"", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", From fc2b67ce9bcc1654fbad2b911eb1f50a51851f7a Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Tue, 14 Oct 2025 14:13:06 -0700 Subject: [PATCH 16/19] Fix environment variable assignment syntax --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 374c5487..678c9441 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -929,9 +929,9 @@ "import os\n", "\n", "# Set some environment variables to customize the Trackio dashboard for experiment tracking\n", - "os.environ["TRACKIO_LOGO_LIGHT_URL"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"", - "os.environ["TRACKIO_LOGO_DARK_URL"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"", - "os.environ["TRACKIO_PLOT_ORDER"] = \"train/loss\"", + "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"\n", + "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"\n", + "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", From 55fc591f3a52ede34fda7f7d0ede0255a1aa664c Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Tue, 14 Oct 2025 14:37:26 -0700 Subject: [PATCH 17/19] Update gpt-oss-(20B)-Fine-tuning.ipynb --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 678c9441..c40774fb 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -949,7 +949,7 @@ " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", - " report_to = \"none\", # Use this for WandB etc\n", + " report_to = \"trackio\",\n", " ),\n", ")" ] From 80ba216d51b0a55ba6800b6466827774d2a62a0f Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Tue, 14 Oct 2025 14:38:07 -0700 Subject: [PATCH 18/19] Add environment variables for Trackio dashboard --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index c40774fb..27775993 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -932,6 +932,7 @@ "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"\n", "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"\n", "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n", + "\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", From 72929fe3ecbb7e18c3a7935f655a896df2b0ce4a Mon Sep 17 00:00:00 2001 From: Abubakar Abid Date: Thu, 16 Oct 2025 12:14:38 -0700 Subject: [PATCH 19/19] Refactor package installation and cleanup code Updated package installation commands and removed environment variable settings. --- nb/gpt-oss-(20B)-Fine-tuning.ipynb | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/nb/gpt-oss-(20B)-Fine-tuning.ipynb b/nb/gpt-oss-(20B)-Fine-tuning.ipynb index 27775993..cd97274d 100644 --- a/nb/gpt-oss-(20B)-Fine-tuning.ipynb +++ b/nb/gpt-oss-(20B)-Fine-tuning.ipynb @@ -51,7 +51,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "%%capture\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2\n!uv pip install trackio<=1.0" + "source": "%%capture\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install --upgrade --no-deps transformers==4.56.2 tokenizers\n!uv pip install --no-deps trl==0.22.2\n!uv pip install \"trackio<=1.0\"" }, { "cell_type": "markdown", @@ -926,12 +926,6 @@ ], "source": [ "from trl import SFTConfig, SFTTrainer\n", - "import os\n", - "\n", - "# Set some environment variables to customize the Trackio dashboard for experiment tracking\n", - "os.environ[\"TRACKIO_LOGO_LIGHT_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png\"\n", - "os.environ[\"TRACKIO_LOGO_DARK_URL\"] = \"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png\"\n", - "os.environ[\"TRACKIO_PLOT_ORDER\"] = \"train/loss\"\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n",