325 lines
29 KiB
Plaintext
325 lines
29 KiB
Plaintext
llama_bin_run\llama-server.exe : ggml_cuda_init: found 2 CUDA
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devices (Total VRAM: 24575 MiB):
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위치 줄:1 문자:1
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+ llama_bin_run\llama-server.exe --model models\Qwen3.5-35B-A3
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B-UD-IQ4_ ...
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+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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~~~~~~~~~
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+ CategoryInfo : NotSpecified: (ggml_cuda_init:.
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..AM: 24575 MiB)::String) [], RemoteException
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+ FullyQualifiedErrorId : NativeCommandError
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Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, V
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MM: yes, VRAM: 12287 MiB
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Device 1: NVIDIA GeForce RTX 3060, compute capability 8.6, V
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MM: yes, VRAM: 12287 MiB
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load_backend: loaded CUDA backend from C:\Users\Variet-Worker\
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Desktop\variet-llm\llama_bin_run\ggml-cuda.dll
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load_backend: loaded RPC backend from C:\Users\Variet-Worker\D
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esktop\variet-llm\llama_bin_run\ggml-rpc.dll
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load_backend: loaded CPU backend from C:\Users\Variet-Worker\D
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esktop\variet-llm\llama_bin_run\ggml-cpu-haswell.dll
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system info: n_threads = 6, n_threads_batch = 6, total_threads
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= 16
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system_info: n_threads = 6 (n_threads_batch = 6) / 16 | CUDA :
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ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1 | PEER_M
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AX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | A
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VX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPEN
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MP = 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-UD-IQ
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4_NL.gguf'
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llama_model_load_from_file_impl: using device CUDA0 (NVIDIA Ge
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Force RTX 3060) (0000:04:00.0) - 11245 MiB free
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llama_model_load_from_file_impl: using device CUDA1 (NVIDIA Ge
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Force RTX 3060) (0000:06:00.0) - 11240 MiB free
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llama_model_loader: loaded meta data with 52 key-value pairs a
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nd 733 tensors from models\Qwen3.5-35B-A3B-UD-IQ4_NL.gguf (ver
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sion GGUF V3 (latest))
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llama_model_loader: Dumping metadata keys/values. Note: KV ove
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rrides do not apply in this output.
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llama_model_loader: - kv 0: general.ar
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chitecture str = qwen35moe
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llama_model_loader: - kv 1: ge
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neral.type str = model
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llama_model_loader: - kv 2: general.samp
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ling.top_k i32 = 20
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llama_model_loader: - kv 3: general.samp
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ling.top_p f32 = 0.950000
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llama_model_loader: - kv 4: general.sam
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pling.temp f32 = 1.000000
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llama_model_loader: - kv 5: ge
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neral.name str = Qwen3.5-35B-A3B
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llama_model_loader: - kv 6: genera
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l.basename str = Qwen3.5-35B-A3B
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llama_model_loader: - kv 7: general.qu
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antized_by str = Unsloth
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llama_model_loader: - kv 8: general.
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size_label str = 35B-A3B
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llama_model_loader: - kv 9: gener
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al.license str = apache-2.0
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llama_model_loader: - kv 10: general.li
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cense.link str = https://huggingface.co/Qwen/Qwen
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3.5-3...
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llama_model_loader: - kv 11: genera
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l.repo_url str = https://huggingface.co/unsloth
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llama_model_loader: - kv 12: general.base_m
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odel.count u32 = 1
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llama_model_loader: - kv 13: general.base_mo
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del.0.name str = Qwen3.5 35B A3B
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llama_model_loader: - kv 14: general.base_model.0.or
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ganization str = Qwen
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llama_model_loader: - kv 15: general.base_model.
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0.repo_url str = https://huggingface.co/Qwen/Qwen
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3.5-3...
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llama_model_loader: - kv 16: ge
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neral.tags arr[str,2] = ["unsloth", "image-text-to-text"
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]
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llama_model_loader: - kv 17: qwen35moe.b
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lock_count u32 = 40
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llama_model_loader: - kv 18: qwen35moe.cont
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ext_length u32 = 262144
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llama_model_loader: - kv 19: qwen35moe.embedd
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ing_length u32 = 2048
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llama_model_loader: - kv 20: qwen35moe.attention.
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head_count u32 = 16
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llama_model_loader: - kv 21: qwen35moe.attention.hea
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d_count_kv u32 = 2
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llama_model_loader: - kv 22: qwen35moe.rope.dimensio
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n_sections arr[i32,4] = [11, 11, 10, 0]
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llama_model_loader: - kv 23: qwen35moe.rope
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.freq_base f32 = 10000000.000000
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llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_r
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ms_epsilon f32 = 0.000001
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llama_model_loader: - kv 25: qwen35moe.ex
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pert_count u32 = 256
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llama_model_loader: - kv 26: qwen35moe.expert_
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used_count u32 = 8
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llama_model_loader: - kv 27: qwen35moe.attention.
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key_length u32 = 256
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llama_model_loader: - kv 28: qwen35moe.attention.va
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lue_length u32 = 256
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llama_model_loader: - kv 29: qwen35moe.expert_feed_forw
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ard_length u32 = 512
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llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_for
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ward_length u32 = 512
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llama_model_loader: - kv 31: qwen35moe.ssm.c
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onv_kernel u32 = 4
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llama_model_loader: - kv 32: qwen35moe.ssm.
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state_size u32 = 128
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llama_model_loader: - kv 33: qwen35moe.ssm.g
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roup_count u32 = 16
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llama_model_loader: - kv 34: qwen35moe.ssm.time
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_step_rank u32 = 32
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llama_model_loader: - kv 35: qwen35moe.ssm.
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inner_size u32 = 4096
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llama_model_loader: - kv 36: qwen35moe.full_attentio
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n_interval u32 = 4
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llama_model_loader: - kv 37: qwen35moe.rope.dimen
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sion_count u32 = 64
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llama_model_loader: - kv 38: tokenizer.
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ggml.model str = gpt2
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llama_model_loader: - kv 39: tokenize
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r.ggml.pre str = qwen35
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llama_model_loader: - kv 40: tokenizer.g
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gml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&",
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"'", ...
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llama_model_loader: - kv 41: tokenizer.ggml.
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token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
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, 1, ...
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llama_model_loader: - kv 42: tokenizer.g
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gml.merges arr[str,247587] = ["휔 휔", "휔휔 휔휔", "i n", "휔 t",..
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.
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llama_model_loader: - kv 43: tokenizer.ggml.eo
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s_token_id u32 = 248046
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llama_model_loader: - kv 44: tokenizer.ggml.paddin
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g_token_id u32 = 248055
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llama_model_loader: - kv 45: tokenizer.cha
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t_template str = {%- set image_count = namespace(
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value...
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llama_model_loader: - kv 46: general.quantizati
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on_version u32 = 2
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llama_model_loader: - kv 47: general
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.file_type u32 = 25
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llama_model_loader: - kv 48: quantize.im
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atrix.file str = Qwen3.5-35B-A3B-GGUF/imatrix_uns
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loth....
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llama_model_loader: - kv 49: quantize.imatr
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ix.dataset str = unsloth_calibration_Qwen3.5-35B-
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A3B.txt
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llama_model_loader: - kv 50: quantize.imatrix.ent
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ries_count u32 = 510
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llama_model_loader: - kv 51: quantize.imatrix.ch
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unks_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 q6_K: 1 tensors
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llama_model_loader: - type iq4_nl: 40 tensors
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llama_model_loader: - type iq3_s: 80 tensors
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print_info: file format = GGUF V3 (latest)
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print_info: file type = IQ4_NL - 4.5 bpw
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print_info: file size = 16.59 GiB (4.11 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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print_info: LF token = 198 '훹'
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print_info: FIM PRE token = 248060 '<|fim_prefix|>'
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print_info: FIM SUF token = 248062 '<|fim_suffix|>'
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print_info: FIM MID token = 248061 '<|fim_middle|>'
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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 = true, 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: CPU_Mapped model buffer size = 515.31 MiB
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load_tensors: CUDA0 model buffer size = 8439.70 MiB
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load_tensors: CUDA1 model buffer size = 8030.62 MiB
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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 = 1024
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llama_context: n_ubatch = 256
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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 MiB
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llama_kv_cache: CUDA0 KV buffer size = 720.00 MiB
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llama_kv_cache: CUDA1 KV buffer size = 720.00 MiB
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llama_kv_cache: size = 1440.00 MiB (262144 cells, 10 layers,
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1/1 seqs), K (q4_0): 720.00 MiB, V (q4_0): 720.00 MiB
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llama_kv_cache: attn_rot_k = 1
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llama_kv_cache: attn_rot_v = 1
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llama_memory_recurrent: CUDA0 RS buffer size = 33.50 M
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iB
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llama_memory_recurrent: CUDA1 RS buffer size = 29.31 M
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iB
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llama_memory_recurrent: size = 62.81 MiB ( 1 cells, 40
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layers, 1 seqs), R (f32): 2.81 MiB, S (f32): 60.00 MiB
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llama_context: pipeline parallelism enabled
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sched_reserve: reserving ...
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sched_reserve: resolving fused Gated Delta Net support:
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sched_reserve: fused Gated Delta Net (autoregressive) enabled
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sched_reserve: fused Gated Delta Net (chunked) enabled
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sched_reserve: CUDA0 compute buffer size = 1199.09 MiB
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sched_reserve: CUDA1 compute buffer size = 767.60 MiB
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sched_reserve: CUDA_Host compute buffer size = 1029.10 MiB
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sched_reserve: graph nodes = 3849
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sched_reserve: graph splits = 3
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sched_reserve: reserve took 171.40 ms, sched copies = 4
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common_init_from_params: warming up the model with an empty ru
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n - please wait ... (--no-warmup to disable)
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srv load_model: initializing slots, n_slots = 1
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common_speculative_is_compat: the target context does not supp
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ort partial sequence removal
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srv load_model: speculative decoding not supported by this
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context
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slot load_model: id 0 | task -1 | new slot, n_ctx = 262144
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srv load_model: prompt cache is enabled, size limit: 8192 M
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iB
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srv load_model: use `--cache-ram 0` to disable the prompt c
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ache
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srv load_model: for more info see https://github.com/ggml-o
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rg/llama.cpp/pull/16391
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srv init: init: --clear-idle requires --kv-unified, d
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isabling
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init: chat template, example_format: '<|im_start|>system
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You are a helpful assistant<|im_end|>
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<|im_start|>user
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Hello<|im_end|>
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<|im_start|>assistant
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Hi there<|im_end|>
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<|im_start|>user
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How are you?<|im_end|>
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<|im_start|>assistant
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<think>
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'
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srv init: init: chat template, thinking = 1
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main: model loaded
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main: server is listening on http://0.0.0.0:8000
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main: starting the main loop...
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srv update_slots: all slots are idle
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