Help me fix please: CUDA error: no kernel image is available for execution on the device

[overallbit]

Trying to install llama cpp to use with LLM nodes on ComfyUI.
it not support GPU RTX 2060?

Using:
Pythin 3.12
Cuda Toolkit 12.8
Not sure why it shows CUDA 13.0 on CMD....

ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 2060, compute capability 7.5, VMM: yes
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 2060) - 5095 MiB free
llama_model_loader: loaded meta data with 50 key-value pairs and 363 tensors from C:\SillyTavern\Kobold Models\LorablatedStock-12B.Q4_K_M.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = LorablatedStock 12B
llama_model_loader: - kv 3: general.basename str = LorablatedStock
llama_model_loader: - kv 4: general.size_label str = 12B
llama_model_loader: - kv 5: general.base_model.count u32 = 3
llama_model_loader: - kv 6: general.base_model.0.name str = HMS Fusion 12B Lorablated
llama_model_loader: - kv 7: general.base_model.0.organization str = Yamatazen
llama_model_loader: - kv 8: general.base_model.0.repo_url str = https://huggingface.co/yamatazen/HMS-...
llama_model_loader: - kv 9: general.base_model.1.name str = ForgottenMaid 12B Lorablated
llama_model_loader: - kv 10: general.base_model.1.organization str = Yamatazen
llama_model_loader: - kv 11: general.base_model.1.repo_url str = https://huggingface.co/yamatazen/Forg...
llama_model_loader: - kv 12: general.base_model.2.name str = FusionEngine 12B Lorablated
llama_model_loader: - kv 13: general.base_model.2.organization str = Yamatazen
llama_model_loader: - kv 14: general.base_model.2.repo_url str = https://huggingface.co/yamatazen/Fusi...
llama_model_loader: - kv 15: general.tags arr[str,3] = ["mergekit", "merge", "lorablated"]
llama_model_loader: - kv 16: general.languages arr[str,2] = ["en", "ja"]
llama_model_loader: - kv 17: llama.block_count u32 = 40
llama_model_loader: - kv 18: llama.context_length u32 = 131072
llama_model_loader: - kv 19: llama.embedding_length u32 = 5120
llama_model_loader: - kv 20: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 21: llama.attention.head_count u32 = 32
llama_model_loader: - kv 22: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 23: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 24: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 25: llama.attention.key_length u32 = 128
llama_model_loader: - kv 26: llama.attention.value_length u32 = 128
llama_model_loader: - kv 27: llama.vocab_size u32 = 131072
llama_model_loader: - kv 28: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 29: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 30: tokenizer.ggml.pre str = tekken
llama_model_loader: - kv 31: tokenizer.ggml.tokens arr[str,131072] = ["", "", "", "[INST]", "[...
llama_model_loader: - kv 32: tokenizer.ggml.token_type arr[i32,131072] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
Exception ignored on calling ctypes callback function: <function llama_log_callback at 0x000001A8DEF7F9C0>
Traceback (most recent call last):
File "C:\ComfyUI\ComfyUI\venv\Lib\site-packages\llama_cpp_logger.py", line 39, in llama_log_callback
print(text.decode("utf-8"), end="", flush=True, file=sys.stderr)
^^^^^^^^^^^^^^^^^^^^
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc4 in position 128: invalid continuation byte
llama_model_loader: - kv 34: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 35: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 36: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 37: tokenizer.ggml.padding_token_id u32 = 10
llama_model_loader: - kv 38: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 39: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 40: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 41: general.quantization_version u32 = 2
llama_model_loader: - kv 42: general.file_type u32 = 15
llama_model_loader: - kv 43: general.url str = https://huggingface.co/mradermacher/L...
llama_model_loader: - kv 44: mradermacher.quantize_version str = 2
llama_model_loader: - kv 45: mradermacher.quantized_by str = mradermacher
llama_model_loader: - kv 46: mradermacher.quantized_at str = 2025-06-06T15:43:16+02:00
llama_model_loader: - kv 47: mradermacher.quantized_on str = back
llama_model_loader: - kv 48: general.source.url str = https://huggingface.co/yamatazen/Lora...
llama_model_loader: - kv 49: mradermacher.convert_type str = hf
llama_model_loader: - type f32: 81 tensors
llama_model_loader: - type q4_K: 241 tensors
llama_model_loader: - type q6_K: 41 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 6.96 GiB (4.88 BPW)
init_tokenizer: initializing tokenizer for type 2
load: control token: 992 '<SPECIAL_992>' is not marked as EOG
load: control token: 993 '<SPECIAL_993>' is not marked as EOG
load: control token: 997 '<SPECIAL_997>' is not marked as EOG
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 1000
load: token to piece cache size = 0.8498 MB
print_info: arch = llama
print_info: vocab_only = 0
print_info: n_ctx_train = 131072
print_info: n_embd = 5120
print_info: n_layer = 40
print_info: n_head = 32
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: n_swa_pattern = 1
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-05
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 14336
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 0
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 131072
print_info: rope_finetuned = unknown
print_info: ssm_d_conv = 0
print_info: ssm_d_inner = 0
print_info: ssm_d_state = 0
print_info: ssm_dt_rank = 0
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 13B
print_info: model params = 12.25 B
print_info: general.name = LorablatedStock 12B
print_info: vocab type = BPE
print_info: n_vocab = 131072
print_info: n_merges = 269443
print_info: BOS token = 1 ''
print_info: EOS token = 2 ''
print_info: UNK token = 0 ''
print_info: PAD token = 10 ''
print_info: LF token = 1010 'Ċ'
print_info: EOG token = 2 ''
print_info: max token length = 150
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: layer 0 assigned to device CPU, is_swa = 0
load_tensors: layer 1 assigned to device CPU, is_swa = 0
load_tensors: layer 2 assigned to device CPU, is_swa = 0
load_tensors: layer 3 assigned to device CPU, is_swa = 0
load_tensors: layer 4 assigned to device CPU, is_swa = 0
load_tensors: layer 5 assigned to device CPU, is_swa = 0
load_tensors: layer 6 assigned to device CPU, is_swa = 0
load_tensors: layer 7 assigned to device CPU, is_swa = 0
load_tensors: layer 8 assigned to device CPU, is_swa = 0
load_tensors: layer 9 assigned to device CPU, is_swa = 0
load_tensors: layer 10 assigned to device CPU, is_swa = 0
load_tensors: layer 11 assigned to device CPU, is_swa = 0
load_tensors: layer 12 assigned to device CPU, is_swa = 0
load_tensors: layer 13 assigned to device CPU, is_swa = 0
load_tensors: layer 14 assigned to device CUDA0, is_swa = 0
load_tensors: layer 15 assigned to device CUDA0, is_swa = 0
load_tensors: layer 16 assigned to device CUDA0, is_swa = 0
load_tensors: layer 17 assigned to device CUDA0, is_swa = 0
load_tensors: layer 18 assigned to device CUDA0, is_swa = 0
load_tensors: layer 19 assigned to device CUDA0, is_swa = 0
load_tensors: layer 20 assigned to device CUDA0, is_swa = 0
load_tensors: layer 21 assigned to device CUDA0, is_swa = 0
load_tensors: layer 22 assigned to device CUDA0, is_swa = 0
load_tensors: layer 23 assigned to device CUDA0, is_swa = 0
load_tensors: layer 24 assigned to device CUDA0, is_swa = 0
load_tensors: layer 25 assigned to device CUDA0, is_swa = 0
load_tensors: layer 26 assigned to device CUDA0, is_swa = 0
load_tensors: layer 27 assigned to device CUDA0, is_swa = 0
load_tensors: layer 28 assigned to device CUDA0, is_swa = 0
load_tensors: layer 29 assigned to device CUDA0, is_swa = 0
load_tensors: layer 30 assigned to device CUDA0, is_swa = 0
load_tensors: layer 31 assigned to device CUDA0, is_swa = 0
load_tensors: layer 32 assigned to device CUDA0, is_swa = 0
load_tensors: layer 33 assigned to device CUDA0, is_swa = 0
load_tensors: layer 34 assigned to device CUDA0, is_swa = 0
load_tensors: layer 35 assigned to device CUDA0, is_swa = 0
load_tensors: layer 36 assigned to device CUDA0, is_swa = 0
load_tensors: layer 37 assigned to device CUDA0, is_swa = 0
load_tensors: layer 38 assigned to device CUDA0, is_swa = 0
load_tensors: layer 39 assigned to device CUDA0, is_swa = 0
load_tensors: layer 40 assigned to device CPU, is_swa = 0
load_tensors: tensor 'token_embd.weight' (q4_K) (and 128 others) cannot be used with preferred buffer type CUDA_Host, using CPU instead
load_tensors: offloading 26 repeating layers to GPU
load_tensors: offloaded 26/41 layers to GPU
load_tensors: CUDA0 model buffer size = 4035.55 MiB
load_tensors: CPU_Mapped model buffer size = 3087.75 MiB
.........................................................................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch = 512
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = 0
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context: CPU output buffer size = 0.50 MiB
create_memory: n_ctx = 4096 (padded)
llama_kv_cache_unified: kv_size = 4096, type_k = 'f16', type_v = 'f16', n_layer = 40, can_shift = 1, padding = 32
llama_kv_cache_unified: layer 0: dev = CPU
llama_kv_cache_unified: layer 1: dev = CPU
llama_kv_cache_unified: layer 2: dev = CPU
llama_kv_cache_unified: layer 3: dev = CPU
llama_kv_cache_unified: layer 4: dev = CPU
llama_kv_cache_unified: layer 5: dev = CPU
llama_kv_cache_unified: layer 6: dev = CPU
llama_kv_cache_unified: layer 7: dev = CPU
llama_kv_cache_unified: layer 8: dev = CPU
llama_kv_cache_unified: layer 9: dev = CPU
llama_kv_cache_unified: layer 10: dev = CPU
llama_kv_cache_unified: layer 11: dev = CPU
llama_kv_cache_unified: layer 12: dev = CPU
llama_kv_cache_unified: layer 13: dev = CPU
llama_kv_cache_unified: layer 14: dev = CUDA0
llama_kv_cache_unified: layer 15: dev = CUDA0
llama_kv_cache_unified: layer 16: dev = CUDA0
llama_kv_cache_unified: layer 17: dev = CUDA0
llama_kv_cache_unified: layer 18: dev = CUDA0
llama_kv_cache_unified: layer 19: dev = CUDA0
llama_kv_cache_unified: layer 20: dev = CUDA0
llama_kv_cache_unified: layer 21: dev = CUDA0
llama_kv_cache_unified: layer 22: dev = CUDA0
llama_kv_cache_unified: layer 23: dev = CUDA0
llama_kv_cache_unified: layer 24: dev = CUDA0
llama_kv_cache_unified: layer 25: dev = CUDA0
llama_kv_cache_unified: layer 26: dev = CUDA0
llama_kv_cache_unified: layer 27: dev = CUDA0
llama_kv_cache_unified: layer 28: dev = CUDA0
llama_kv_cache_unified: layer 29: dev = CUDA0
llama_kv_cache_unified: layer 30: dev = CUDA0
llama_kv_cache_unified: layer 31: dev = CUDA0
llama_kv_cache_unified: layer 32: dev = CUDA0
llama_kv_cache_unified: layer 33: dev = CUDA0
llama_kv_cache_unified: layer 34: dev = CUDA0
llama_kv_cache_unified: layer 35: dev = CUDA0
llama_kv_cache_unified: layer 36: dev = CUDA0
llama_kv_cache_unified: layer 37: dev = CUDA0
llama_kv_cache_unified: layer 38: dev = CUDA0
llama_kv_cache_unified: layer 39: dev = CUDA0
llama_kv_cache_unified: CUDA0 KV buffer size = 416.00 MiB
llama_kv_cache_unified: CPU KV buffer size = 224.00 MiB
llama_kv_cache_unified: KV self size = 640.00 MiB, K (f16): 320.00 MiB, V (f16): 320.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 2
llama_context: max_nodes = 65536
llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
llama_context: reserving graph for n_tokens = 512, n_seqs = 1
llama_context: reserving graph for n_tokens = 1, n_seqs = 1
llama_context: reserving graph for n_tokens = 512, n_seqs = 1
llama_context: CUDA0 compute buffer size = 791.00 MiB
llama_context: CUDA_Host compute buffer size = 18.01 MiB
llama_context: graph nodes = 1366
llama_context: graph splits = 158 (with bs=512), 3 (with bs=1)
CUDA : ARCHS = 860,890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
Model metadata: {'mradermacher.convert_type': 'hf', 'general.name': 'LorablatedStock 12B', 'general.architecture': 'llama', 'general.base_model.0.name': 'HMS Fusion 12B Lorablated', 'general.type': 'model', 'general.basename': 'LorablatedStock', 'general.base_model.0.organization': 'Yamatazen', 'mradermacher.quantized_on': 'back', 'general.size_label': '12B', 'general.base_model.2.name': 'FusionEngine 12B Lorablated', 'general.base_model.count': '3', 'llama.attention.value_length': '128', 'general.base_model.0.repo_url': 'https://huggingface.co/yamatazen/HMS-Fusion-12B-Lorablated', 'general.base_model.1.name': 'ForgottenMaid 12B Lorablated', 'general.base_model.1.organization': 'Yamatazen', 'general.base_model.1.repo_url': 'https://huggingface.co/yamatazen/ForgottenMaid-12B-Lorablated', 'general.base_model.2.organization': 'Yamatazen', 'tokenizer.ggml.add_space_prefix': 'false', 'tokenizer.ggml.pre': 'tekken', 'general.base_model.2.repo_url': 'https://huggingface.co/yamatazen/FusionEngine-12B-Lorablated', 'llama.block_count': '40', 'llama.context_length': '131072', 'llama.embedding_length': '5120', 'llama.feed_forward_length': '14336', 'llama.attention.head_count': '32', 'general.file_type': '15', 'tokenizer.ggml.eos_token_id': '2', 'llama.attention.head_count_kv': '8', 'llama.rope.freq_base': '1000000.000000', 'mradermacher.quantize_version': '2', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.attention.key_length': '128', 'llama.vocab_size': '131072', 'llama.rope.dimension_count': '128', 'tokenizer.ggml.model': 'gpt2', 'general.source.url': 'https://huggingface.co/yamatazen/LorablatedStock-12B', 'general.quantization_version': '2', 'tokenizer.ggml.bos_token_id': '1', 'mradermacher.quantized_by': 'mradermacher', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.padding_token_id': '10', 'tokenizer.ggml.add_bos_token': 'true', 'tokenizer.ggml.add_eos_token': 'false', 'general.url': 'https://huggingface.co/mradermacher/LorablatedStock-12B-GGUF', 'mradermacher.quantized_at': '2025-06-06T15:43:16+02:00'}
Using fallback chat format: llama-2
ggml_cuda_compute_forward: RMS_NORM failed
CUDA error: no kernel image is available for execution on the device
current device: 0, in function ggml_cuda_compute_forward at C:\Users\bpfit\Downloads\llama_cpp_python-0.3.9\llama_cpp_python-0.3.9\vendor\llama.cpp\ggml\src\ggml-cuda\ggml-cuda.cu:2353
err
C:\Users\bpfit\Downloads\llama_cpp_python-0.3.9\llama_cpp_python-0.3.9\vendor\llama.cpp\ggml\src\ggml-cuda\ggml-cuda.cu:75: CUDA error

C:\Users\bpfit\Downloads\llama_cpp_python-0.3.9..... The username of my PC is not bpfit.. not sure why its showing it.