307 lines
27 KiB
Plaintext
307 lines
27 KiB
Plaintext
llama_bin_run\llama-server.exe : ggml_cuda_init: found 2 C
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UDA devices (Total VRAM: 24575 MiB):
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위치 줄:1 문자:58
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+ ... rt-Sleep 3; llama_bin_run\llama-server.exe --model m
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odels\Qwen3.5-35B ...
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+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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~~~~~~~~~~~~~~~~~
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+ CategoryInfo : NotSpecified: (ggml_cuda_in
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it:...AM: 24575 MiB)::String) [], RemoteException
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+ FullyQualifiedErrorId : NativeCommandError
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Device 0: NVIDIA GeForce RTX 3060, compute capability 8.
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6, VMM: yes, VRAM: 12287 MiB
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Device 1: NVIDIA GeForce RTX 3060, compute capability 8.
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6, VMM: yes, VRAM: 12287 MiB
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load_backend: loaded CUDA backend from C:\Users\Variet-Wor
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ker\Desktop\variet-llm\llama_bin_run\ggml-cuda.dll
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load_backend: loaded RPC backend from C:\Users\Variet-Work
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er\Desktop\variet-llm\llama_bin_run\ggml-rpc.dll
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load_backend: loaded CPU backend from C:\Users\Variet-Work
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er\Desktop\variet-llm\llama_bin_run\ggml-cpu-haswell.dll
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system info: n_threads = 6, n_threads_batch = 6, total_thr
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eads = 16
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system_info: n_threads = 6 (n_threads_batch = 6) / 16 | CU
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DA : ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1
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| PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 |
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AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAM
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AFILE = 1 | OPENMP = 1 | REPACK = 1 |
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Running without SSL
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init: using 15 threads for HTTP server
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start: binding port with default address family
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main: loading model
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srv load_model: loading model 'models\Qwen3.5-35B-A3B-Q
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4_K_M.gguf'
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common_init_result: fitting params to device memory, for b
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ugs during this step try to reproduce them with -fit off,
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or provide --verbose logs if the bug only occurs with -fit
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on
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llama_params_fit_impl: projected memory use with initial p
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arameters [MiB]:
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llama_params_fit_impl: - CUDA0 (NVIDIA GeForce RTX 3060)
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: 12287 total, 12398 used, -1189 free vs. target of 1
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024
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llama_params_fit_impl: - CUDA1 (NVIDIA GeForce RTX 3060)
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: 12287 total, 10562 used, 649 free vs. target of 1
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024
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llama_params_fit_impl: projected to use 22961 MiB of devic
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e memory vs. 22421 MiB of free device memory
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llama_params_fit_impl: cannot meet free memory targets on
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all devices, need to use 2588 MiB less in total
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llama_params_fit_impl: context size set by user to 262144
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-> no change
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llama_params_fit: failed to fit params to free device memo
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ry: n_gpu_layers already set by user to 999, abort
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llama_params_fit: fitting params to free memory took 0.61
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seconds
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llama_model_load_from_file_impl: using device CUDA0 (NVIDI
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A GeForce RTX 3060) (0000:04:00.0) - 11245 MiB free
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llama_model_load_from_file_impl: using device CUDA1 (NVIDI
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A GeForce RTX 3060) (0000:06:00.0) - 11240 MiB free
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llama_model_loader: loaded meta data with 52 key-value pai
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rs and 733 tensors from models\Qwen3.5-35B-A3B-Q4_K_M.gguf
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(version GGUF V3 (latest))
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llama_model_loader: Dumping metadata keys/values. Note: KV
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overrides do not apply in this output.
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llama_model_loader: - kv 0: genera
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l.architecture str = qwen35moe
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llama_model_loader: - kv 1:
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general.type str = model
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llama_model_loader: - kv 2: general.
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sampling.top_k i32 = 20
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llama_model_loader: - kv 3: general.
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sampling.top_p f32 = 0.950000
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llama_model_loader: - kv 4: general
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.sampling.temp f32 = 1.000000
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llama_model_loader: - kv 5:
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general.name str = Qwen3.5-35B-A3B
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llama_model_loader: - kv 6: ge
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neral.basename str = Qwen3.5-35B-A3B
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llama_model_loader: - kv 7: genera
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l.quantized_by str = Unsloth
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llama_model_loader: - kv 8: gene
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ral.size_label str = 35B-A3B
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llama_model_loader: - kv 9: g
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eneral.license str = apache-2.0
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llama_model_loader: - kv 10: genera
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l.license.link str = https://huggingface.co/Q
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wen/Qwen3.5-3...
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llama_model_loader: - kv 11: ge
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neral.repo_url str = https://huggingface.co/u
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nsloth
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llama_model_loader: - kv 12: general.ba
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se_model.count u32 = 1
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llama_model_loader: - kv 13: general.bas
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e_model.0.name str = Qwen3.5 35B A3B
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llama_model_loader: - kv 14: general.base_model.
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0.organization str = Qwen
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llama_model_loader: - kv 15: general.base_mo
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del.0.repo_url str = https://huggingface.co/Q
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wen/Qwen3.5-3...
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llama_model_loader: - kv 16:
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general.tags arr[str,2] = ["unsloth", "image-text-
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to-text"]
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llama_model_loader: - kv 17: qwen35m
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oe.block_count u32 = 40
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llama_model_loader: - kv 18: qwen35moe.
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context_length u32 = 262144
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llama_model_loader: - kv 19: qwen35moe.em
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bedding_length u32 = 2048
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llama_model_loader: - kv 20: qwen35moe.attent
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ion.head_count u32 = 16
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llama_model_loader: - kv 21: qwen35moe.attention
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.head_count_kv u32 = 2
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llama_model_loader: - kv 22: qwen35moe.rope.dime
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nsion_sections arr[i32,4] = [11, 11, 10, 0]
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llama_model_loader: - kv 23: qwen35moe.
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rope.freq_base f32 = 10000000.000000
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llama_model_loader: - kv 24: qwen35moe.attention.layer_no
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rm_rms_epsilon f32 = 0.000001
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llama_model_loader: - kv 25: qwen35mo
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e.expert_count u32 = 256
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llama_model_loader: - kv 26: qwen35moe.exp
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ert_used_count u32 = 8
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llama_model_loader: - kv 27: qwen35moe.attent
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ion.key_length u32 = 256
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llama_model_loader: - kv 28: qwen35moe.attentio
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n.value_length u32 = 256
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llama_model_loader: - kv 29: qwen35moe.expert_feed_
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forward_length u32 = 512
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llama_model_loader: - kv 30: qwen35moe.expert_shared_feed
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_forward_length u32 = 512
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llama_model_loader: - kv 31: qwen35moe.s
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sm.conv_kernel u32 = 4
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llama_model_loader: - kv 32: qwen35moe.
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ssm.state_size u32 = 128
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llama_model_loader: - kv 33: qwen35moe.s
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sm.group_count u32 = 16
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llama_model_loader: - kv 34: qwen35moe.ssm.
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time_step_rank u32 = 32
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llama_model_loader: - kv 35: qwen35moe.
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ssm.inner_size u32 = 4096
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llama_model_loader: - kv 36: qwen35moe.full_atte
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ntion_interval u32 = 4
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llama_model_loader: - kv 37: qwen35moe.rope.d
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imension_count u32 = 64
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llama_model_loader: - kv 38: tokeni
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zer.ggml.model str = gpt2
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llama_model_loader: - kv 39: toke
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nizer.ggml.pre str = qwen35
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llama_model_loader: - kv 40: tokeniz
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er.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%
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", "&", "'", ...
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llama_model_loader: - kv 41: tokenizer.g
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gml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, ...
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llama_model_loader: - kv 42: tokeniz
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er.ggml.merges arr[str,247587] = ["휔 휔", "휔휔 휔휔", "i n",
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"휔 t",...
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llama_model_loader: - kv 43: tokenizer.ggm
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l.eos_token_id u32 = 248046
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llama_model_loader: - kv 44: tokenizer.ggml.pa
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dding_token_id u32 = 248055
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llama_model_loader: - kv 45: tokenizer
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.chat_template str = {%- set image_count = na
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mespace(value...
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llama_model_loader: - kv 46: general.quanti
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zation_version u32 = 2
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llama_model_loader: - kv 47: gen
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eral.file_type u32 = 15
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llama_model_loader: - kv 48: quantiz
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e.imatrix.file str = Qwen3.5-35B-A3B-GGUF/ima
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trix_unsloth....
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llama_model_loader: - kv 49: quantize.i
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matrix.dataset str = unsloth_calibration_Qwen
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3.5-35B-A3B.txt
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llama_model_loader: - kv 50: quantize.imatrix
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.entries_count u32 = 510
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llama_model_loader: - kv 51: quantize.imatri
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x.chunks_count u32 = 76
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llama_model_loader: - type f32: 301 tensors
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llama_model_loader: - type q8_0: 311 tensors
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llama_model_loader: - type q4_K: 80 tensors
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llama_model_loader: - type q5_K: 40 tensors
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llama_model_loader: - type q6_K: 1 tensors
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print_info: file format = GGUF V3 (latest)
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print_info: file type = Q4_K - Medium
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print_info: file size = 20.49 GiB (5.08 BPW)
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load: 0 unused tokens
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load: printing all EOG tokens:
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load: - 248044 ('<|endoftext|>')
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load: - 248046 ('<|im_end|>')
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load: - 248063 ('<|fim_pad|>')
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load: - 248064 ('<|repo_name|>')
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load: - 248065 ('<|file_sep|>')
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load: special tokens cache size = 33
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load: token to piece cache size = 1.7581 MB
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print_info: arch = qwen35moe
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print_info: vocab_only = 0
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print_info: no_alloc = 0
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print_info: n_ctx_train = 262144
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print_info: n_embd = 2048
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print_info: n_embd_inp = 2048
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print_info: n_layer = 40
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print_info: n_head = 16
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print_info: n_head_kv = 2
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print_info: n_rot = 64
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print_info: n_swa = 0
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print_info: is_swa_any = 0
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print_info: n_embd_head_k = 256
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print_info: n_embd_head_v = 256
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print_info: n_gqa = 8
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print_info: n_embd_k_gqa = 512
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print_info: n_embd_v_gqa = 512
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print_info: f_norm_eps = 0.0e+00
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print_info: f_norm_rms_eps = 1.0e-06
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print_info: f_clamp_kqv = 0.0e+00
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print_info: f_max_alibi_bias = 0.0e+00
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print_info: f_logit_scale = 0.0e+00
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print_info: f_attn_scale = 0.0e+00
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print_info: n_ff = 0
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print_info: n_expert = 256
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print_info: n_expert_used = 8
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print_info: n_expert_groups = 0
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print_info: n_group_used = 0
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print_info: causal attn = 1
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print_info: pooling type = -1
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print_info: rope type = 40
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print_info: rope scaling = linear
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print_info: freq_base_train = 10000000.0
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print_info: freq_scale_train = 1
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print_info: n_ctx_orig_yarn = 262144
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print_info: rope_yarn_log_mul = 0.0000
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print_info: rope_finetuned = unknown
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print_info: mrope sections = [11, 11, 10, 0]
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print_info: ssm_d_conv = 4
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print_info: ssm_d_inner = 4096
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print_info: ssm_d_state = 128
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print_info: ssm_dt_rank = 32
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print_info: ssm_n_group = 16
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print_info: ssm_dt_b_c_rms = 0
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print_info: model type = 35B.A3B
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print_info: model params = 34.66 B
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print_info: general.name = Qwen3.5-35B-A3B
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print_info: vocab type = BPE
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print_info: n_vocab = 248320
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print_info: n_merges = 247587
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print_info: BOS token = 11 ','
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print_info: EOS token = 248046 '<|im_end|>'
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print_info: EOT token = 248046 '<|im_end|>'
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print_info: PAD token = 248055 '<|vision_pad|>
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'
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print_info: LF token = 198 '훹'
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print_info: FIM PRE token = 248060 '<|fim_prefix|>
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'
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print_info: FIM SUF token = 248062 '<|fim_suffix|>
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'
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print_info: FIM MID token = 248061 '<|fim_middle|>
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'
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print_info: FIM PAD token = 248063 '<|fim_pad|>'
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print_info: FIM REP token = 248064 '<|repo_name|>'
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print_info: FIM SEP token = 248065 '<|file_sep|>'
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print_info: EOG token = 248044 '<|endoftext|>'
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print_info: EOG token = 248046 '<|im_end|>'
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print_info: EOG token = 248063 '<|fim_pad|>'
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print_info: EOG token = 248064 '<|repo_name|>'
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print_info: EOG token = 248065 '<|file_sep|>'
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print_info: max token length = 256
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load_tensors: loading model tensors, this can take a while
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... (mmap = false, direct_io = false)
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load_tensors: offloading output layer to GPU
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load_tensors: offloading 39 repeating layers to GPU
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load_tensors: offloaded 41/41 layers to GPU
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load_tensors: CUDA0 model buffer size = 11043.17 Mi
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B
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load_tensors: CUDA1 model buffer size = 9427.15 Mi
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B
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load_tensors: CUDA_Host model buffer size = 515.31 Mi
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B
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..........................................................
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.......................................
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common_init_result: added <|endoftext|> logit bias = -inf
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common_init_result: added <|im_end|> logit bias = -inf
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common_init_result: added <|fim_pad|> logit bias = -inf
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common_init_result: added <|repo_name|> logit bias = -inf
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common_init_result: added <|file_sep|> logit bias = -inf
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llama_context: constructing llama_context
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llama_context: n_seq_max = 1
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llama_context: n_ctx = 262144
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llama_context: n_ctx_seq = 262144
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llama_context: n_batch = 512
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llama_context: n_ubatch = 128
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llama_context: causal_attn = 1
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llama_context: flash_attn = enabled
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llama_context: kv_unified = false
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llama_context: freq_base = 10000000.0
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llama_context: freq_scale = 1
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llama_context: CUDA_Host output buffer size = 0.95 M
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iB
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ggml_backend_cuda_buffer_type_alloc_buffer: allocating 720
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.00 MiB on device 0: cudaMalloc failed: out of memory
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alloc_tensor_range: failed to allocate CUDA0 buffer of siz
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e 754974720
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llama_init_from_model: failed to initialize the context: f
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ailed to allocate buffer for kv cache
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