diff --git a/nb/Qwen3_(0.6B)-Reasoning-Conversational-ExecuTorch.ipynb b/nb/Qwen3_(0.6B)-Reasoning-Conversational-ExecuTorch.ipynb
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@@ -0,0 +1,8405 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
+        "<div class=\"align-center\">\n",
+        "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
+        "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
+        "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
+        "</div>\n",
+        "\n",
+        "To install Unsloth your local device, follow [our guide](https://docs.unsloth.ai/get-started/install-and-update). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\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": [
+        "### Installation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "import os, re\n",
+        "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n",
+        "    !pip install unsloth\n",
+        "else:\n",
+        "    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n",
+        "    import torch; v = re.match(r\"[0-9]{1,}\\.[0-9]{1,}\", str(torch.__version__)).group(0)\n",
+        "    xformers = \"xformers==\" + (\"0.0.33.post1\" if v==\"2.9\" else \"0.0.32.post2\" if v==\"2.8\" else \"0.0.29.post3\")\n",
+        "    !pip install --no-deps bitsandbytes accelerate {xformers} peft trl triton cut_cross_entropy unsloth_zoo\n",
+        "    !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n",
+        "    !pip install --no-deps unsloth\n",
+        "!pip install transformers==4.56.2\n",
+        "!pip install --no-deps trl==0.22.2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iajq1W8ipjyK"
+      },
+      "source": [
+        "### Unsloth"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
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+        },
+        "id": "QmUBVEnvCDJv",
+        "output": {
+          "id": 892318013456652,
+          "loadingStatus": "loaded"
+        },
+        "outputId": "ad27d22a-d0a6-4659-be63-60bf3b3561b7"
+      },
+      "outputs": [],
+      "source": [
+        "from unsloth import FastLanguageModel\n",
+        "from unsloth.chat_templates import (\n",
+        "    get_chat_template,\n",
+        ")\n",
+        "import torch\n",
+        "\n",
+        "MODEL_ID = \"unsloth/Qwen3-0.6B\"\n",
+        "QAT_SCHEME = \"int8-int4\"\n",
+        "\n",
+        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
+        "    model_name = MODEL_ID,\n",
+        "    max_seq_length = 2048,\n",
+        "    dtype = torch.bfloat16,\n",
+        "    load_in_4bit = False,\n",
+        "    full_finetuning = True,\n",
+        "    # ExecuTorch CPU quantization scheme\n",
+        "    # Quantize embedding to 8-bits, and quantize linear layers to 4-bits\n",
+        "    # with 8-bit dynamically quantized activations\n",
+        "    qat_scheme = QAT_SCHEME,\n",
+        ")\n",
+        "tokenizer = get_chat_template(tokenizer, chat_template = \"qwen3\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vITh0KVJ10qX"
+      },
+      "source": [
+        "<a name=\"Data\"></a>\n",
+        "### Data Prep\n",
+        "Qwen3 has both reasoning and a non reasoning mode. So, we should use 2 datasets:\n",
+        "\n",
+        "1. We use the [Open Math Reasoning]() dataset which was used to win the [AIMO](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2/leaderboard) (AI Mathematical Olympiad - Progress Prize 2) challenge! We sample 10% of verifiable reasoning traces that used DeepSeek R1, and whicht got > 95% accuracy.\n",
+        "\n",
+        "2. We also leverage [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. But we need to convert it to HuggingFace's normal multiturn format as well."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209,
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+            "100a0b5f637b4dc1a1da400e8f363678"
+          ]
+        },
+        "id": "5kyTw2n1edte",
+        "outputId": "3bd7207b-c08d-486b-949e-ee4868a366a2"
+      },
+      "outputs": [],
+      "source": [
+        "from datasets import load_dataset\n",
+        "reasoning_dataset = load_dataset(\"unsloth/OpenMathReasoning-mini\", split = \"cot\")\n",
+        "non_reasoning_dataset = load_dataset(\"mlabonne/FineTome-100k\", split = \"train\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PTZICZtie3lQ"
+      },
+      "source": [
+        "Let's see the structure of both datasets:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DjgH3lt0e2Sz",
+        "outputId": "2be3f7c6-7353-42c9-d676-95949db681d9"
+      },
+      "outputs": [],
+      "source": [
+        "reasoning_dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_zoaygOAe3I2",
+        "outputId": "b883173b-8212-407c-f375-64eae1ed4772"
+      },
+      "outputs": [],
+      "source": [
+        "non_reasoning_dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YX8H3urDe00l"
+      },
+      "source": [
+        "We now convert the reasoning dataset into conversational format:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LjY75GoYUCB8"
+      },
+      "outputs": [],
+      "source": [
+        "def generate_conversation(examples):\n",
+        "    problems  = examples[\"problem\"]\n",
+        "    solutions = examples[\"generated_solution\"]\n",
+        "    conversations = []\n",
+        "    for problem, solution in zip(problems, solutions):\n",
+        "        conversations.append([\n",
+        "            {\"role\" : \"user\",      \"content\" : problem},\n",
+        "            {\"role\" : \"assistant\", \"content\" : solution},\n",
+        "        ])\n",
+        "    return { \"conversations\": conversations, }"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "5b3081e536ac4a258fe5db59bb927ca2",
+            "acbef56d18ae4b86ad0d4b79210db204",
+            "2857e876d47a49bbadf3494b1864c1bf",
+            "87ca88b487414767af6533125e688186",
+            "b1e286de944749ec8f5b4ce03df7b7e5",
+            "cde3541ff2444f55a1651b8992e53da1",
+            "7b21ad18c43d4509810204b8a4787b64",
+            "2b9748337a1c4291ac882194b16eb032",
+            "d1b000b08af549689d1be5f0289bf2c3",
+            "a1e4e03e40b2480388b67dcb7d2120c3",
+            "8bf8b1e60de142a6845aaadbe64fcb85"
+          ]
+        },
+        "id": "gbh19fTOfHDB",
+        "outputId": "66be18a9-9818-4ce3-d4c0-58bfdad79b7c"
+      },
+      "outputs": [],
+      "source": [
+        "reasoning_conversations = tokenizer.apply_chat_template(\n",
+        "    list(reasoning_dataset.map(generate_conversation, batched = True)[\"conversations\"]),\n",
+        "    tokenize = False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XTexROzQfJn5"
+      },
+      "source": [
+        "Let's see the first transformed row:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "id": "mkj4c6NrfIz3",
+        "outputId": "03e0a2e7-1cc5-45ec-ed3b-01828e68d688"
+      },
+      "outputs": [],
+      "source": [
+        "reasoning_conversations[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5OMhyEXkfM5e"
+      },
+      "source": [
+        "Next we take the non reasoning dataset and convert it to conversational format as well.\n",
+        "\n",
+        "We have to use Unsloth's `standardize_sharegpt` function to fix up the format of the dataset first."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 77,
+          "referenced_widgets": [
+            "8cdf258cb0554d64a4aceed488b6361b",
+            "53f8155387394238b443d54cffe9a363",
+            "852a10d472ad48a19203ded79cf30662",
+            "9ced22ec1fc54b4e9e7b7ab1aae0c940",
+            "a6a8ff65be444bc3999c28f53d9b46f4",
+            "09d254a8222d42b093ff8f229a6fe503",
+            "daa8753d49d7458cab39dd1524b20356",
+            "c2d8db7e564a40499d58f0d9c11b01a7",
+            "5a7feb07c0124b9ab00900eb25efa616",
+            "455c651682fd42eda5a25ce06560893f",
+            "a56f6c918e064278a0217c53e8cc2f6e"
+          ]
+        },
+        "id": "nXBFaeQHfSxp",
+        "outputId": "39a49b43-58c8-499a-a870-63f5c7e60189"
+      },
+      "outputs": [],
+      "source": [
+        "from unsloth.chat_templates import standardize_sharegpt\n",
+        "dataset = standardize_sharegpt(non_reasoning_dataset)\n",
+        "\n",
+        "non_reasoning_conversations = tokenizer.apply_chat_template(\n",
+        "    list(dataset[\"conversations\"]),\n",
+        "    tokenize = False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q9FcosGvfdNr"
+      },
+      "source": [
+        "Let's see the first row"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "id": "pb0hbEekfeqf",
+        "outputId": "1f7d27a8-d2a4-4a41-8730-c92ddadb8adb"
+      },
+      "outputs": [],
+      "source": [
+        "non_reasoning_conversations[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c_0L18QMfot4"
+      },
+      "source": [
+        "Now let's see how long both datasets are:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "unDFuUq1foWj",
+        "outputId": "ad1e986e-32ed-4f51-a7df-34499501efc4"
+      },
+      "outputs": [],
+      "source": [
+        "print(len(reasoning_conversations))\n",
+        "print(len(non_reasoning_conversations))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dgknnOf7fn3e"
+      },
+      "source": [
+        "The non reasoning dataset is much longer. Let's assume we want the model to retain some reasoning capabilities, but we specifically want a chat model.\n",
+        "\n",
+        "Let's define a ratio of chat only data. The goal is to define some mixture of both sets of data.\n",
+        "\n",
+        "Let's select 75% reasoning and 25% chat based:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_szfriCBgCkU"
+      },
+      "outputs": [],
+      "source": [
+        "chat_percentage = 0.25"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DANuEJA7gL58"
+      },
+      "source": [
+        "Let's sample the reasoning dataset by 75% (or whatever is 100% - chat_percentage)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7-e0KO9GgFy3",
+        "outputId": "62c4b67d-b6b8-4f9c-9934-2bd948f9f5be"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "non_reasoning_subset = pd.Series(non_reasoning_conversations)\n",
+        "non_reasoning_subset = non_reasoning_subset.sample(\n",
+        "    int(len(reasoning_conversations)*(chat_percentage/(1 - chat_percentage))),\n",
+        "    random_state = 2407,\n",
+        ")\n",
+        "print(len(reasoning_conversations))\n",
+        "print(len(non_reasoning_subset))\n",
+        "print(len(non_reasoning_subset) / (len(non_reasoning_subset) + len(reasoning_conversations)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qR-4prS_gVel"
+      },
+      "source": [
+        "Finally combine both datasets:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jfV47_SXgXH4"
+      },
+      "outputs": [],
+      "source": [
+        "data = pd.concat([\n",
+        "    pd.Series(reasoning_conversations),\n",
+        "    pd.Series(non_reasoning_subset)\n",
+        "])\n",
+        "data.name = \"text\"\n",
+        "\n",
+        "from datasets import Dataset\n",
+        "combined_dataset = Dataset.from_pandas(pd.DataFrame(data))\n",
+        "combined_dataset = combined_dataset.shuffle(seed = 3407)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "idAEIeSQ3xdS"
+      },
+      "source": [
+        "<a name=\"Train\"></a>\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": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 77,
+          "referenced_widgets": [
+            "109c736c8b99496e8721f9595c54eb6c",
+            "f89daea726144ded892113a161649b64",
+            "9f0108d9201f40599facfccf668a6e84",
+            "927c60cd1f0d4a32a1ff9427a96a3246",
+            "fb6f38d9dd1e49ec8fdfb7ca7ea363a2",
+            "6303ec778b1d49dd980542a191261961",
+            "5d66a72e82e54d9e8367ca8a2469dd0a",
+            "e17016c458a14736bd56ba55d7168f32",
+            "1d31954fe1804140bcac71e3d4102fdc",
+            "a7077f3352b246ddbbca391c08c48b4a",
+            "a1c374126fb144e68060e4fedab51046"
+          ]
+        },
+        "id": "95_Nn-89DhsL",
+        "outputId": "4aee3090-cf58-4497-979b-cd41cbc5fd62"
+      },
+      "outputs": [],
+      "source": [
+        "from trl import SFTTrainer, SFTConfig\n",
+        "trainer = SFTTrainer(\n",
+        "    model = model,\n",
+        "    tokenizer = tokenizer,\n",
+        "    train_dataset = combined_dataset,\n",
+        "    eval_dataset = None, # Can set up evaluation!\n",
+        "    args = SFTConfig(\n",
+        "        dataset_text_field = \"text\",\n",
+        "        per_device_train_batch_size = 2,\n",
+        "        gradient_accumulation_steps = 4, # Use GA to mimic batch size!\n",
+        "        warmup_steps = 5,\n",
+        "        # num_train_epochs = 1, # Set this for 1 full training run.\n",
+        "        max_steps = 100,\n",
+        "        learning_rate = 5e-5,\n",
+        "        logging_steps = 1,\n",
+        "        optim = \"adamw_8bit\",\n",
+        "        weight_decay = 0.001,\n",
+        "        lr_scheduler_type = \"linear\",\n",
+        "        seed = 3407,\n",
+        "        report_to = \"none\", # Use TrackIO/WandB etc\n",
+        "    ),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M9fa371ShyhB"
+      },
+      "source": [
+        "Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "yqxqAZ7KJ4oL",
+        "outputId": "4b644b12-626b-45c7-fb11-89825ae13bd2"
+      },
+      "outputs": [],
+      "source": [
+        "trainer_stats = trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ekOmTR1hSNcr"
+      },
+      "source": [
+        "<a name=\"Inference\"></a>\n",
+        "### Inference\n",
+        "Let's run the model via Unsloth native inference! According to the `Qwen-3` team, the recommended settings for reasoning inference are `temperature = 0.6, top_p = 0.95, top_k = 20`\n",
+        "\n",
+        "For normal chat based inference, `temperature = 0.7, top_p = 0.8, top_k = 20`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kR3gIAX-SM2q",
+        "outputId": "b813e560-8e4c-4491-c8be-18067bc07639"
+      },
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    {\"role\" : \"user\", \"content\" : \"Solve (x + 2)^2 = 0.\"}\n",
+        "]\n",
+        "text = tokenizer.apply_chat_template(\n",
+        "    messages,\n",
+        "    tokenize = False,\n",
+        "    add_generation_prompt = True, # Must add for generation\n",
+        "    enable_thinking = False, # Disable thinking\n",
+        ")\n",
+        "\n",
+        "from transformers import TextStreamer\n",
+        "_ = model.generate(\n",
+        "    **tokenizer(text, return_tensors = \"pt\").to(\"cuda\"),\n",
+        "    max_new_tokens = 256, # Increase for longer outputs!\n",
+        "    temperature = 0.7, top_p = 0.8, top_k = 20, # For non thinking\n",
+        "    streamer = TextStreamer(tokenizer, skip_prompt = True),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j873RMcEi9uq",
+        "outputId": "3b358da9-aedd-48e3-a345-1ed0ca0bd3fa"
+      },
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    {\"role\" : \"user\", \"content\" : \"Solve (x + 2)^2 = 0.\"}\n",
+        "]\n",
+        "text = tokenizer.apply_chat_template(\n",
+        "    messages,\n",
+        "    tokenize = False,\n",
+        "    add_generation_prompt = True, # Must add for generation\n",
+        "    enable_thinking = True, # Disable thinking\n",
+        ")\n",
+        "\n",
+        "from transformers import TextStreamer\n",
+        "_ = model.generate(\n",
+        "    **tokenizer(text, return_tensors = \"pt\").to(\"cuda\"),\n",
+        "    max_new_tokens = 1024, # Increase for longer outputs!\n",
+        "    temperature = 0.6, top_p = 0.95, top_k = 20, # For thinking\n",
+        "    streamer = TextStreamer(tokenizer, skip_prompt = True),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uMuVrWbjAzhc"
+      },
+      "source": [
+        "<a name=\"Save\"></a>\n",
+        "### Saving\n",
+        "The final model can be saved locally or pushed to the Hub to share with the community."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "upcOlWe7A1vc",
+        "outputId": "0a9c6608-d1f5-4779-8ad4-7e3a46e2258d"
+      },
+      "outputs": [],
+      "source": [
+        "model_name = MODEL_ID.split(\"/\")[-1]\n",
+        "save_to = f\"{model_name}-{QAT_SCHEME}-unsloth\"\n",
+        "\n",
+        "# Save locally - this auto-detects QAT models and handles the conversion\n",
+        "model.save_pretrained_torchao(save_to, tokenizer=tokenizer)\n",
+        "\n",
+        "# Or push to hub under your username\n",
+        "# from huggingface_hub import get_token, whoami\n",
+        "# def _get_username():\n",
+        "#     token = get_token()\n",
+        "#     username = whoami(token=token)[\"name\"]\n",
+        "#     return username\n",
+        "# username = _get_username()\n",
+        "# model.save_pretrained_torchao(f\"{username}/{save_to}\", tokenizer=tokenizer, push_to_hub=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once saved, we can export the model checkpoint to ExecuTorch.  Follow the directions [@nocommit TBD](TBD)."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "fileHeader": "",
+    "fileUid": "6fb77cfe-99ad-4198-9e72-d40c5b6b7578",
+    "isAdHoc": false,
+    "kernelspec": {
+      "display_name": "unsloth (conda)",
+      "language": "python",
+      "name": "conda_unsloth"
+    },
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