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main: build = 3086 (554c247c)
main: built with cc (Ubuntu 13.2.0-23ubuntu4) 13.2.0 for x86_64-linux-gnu
main: seed  = 1717697873
llama_model_loader: loaded meta data with 21 key-value pairs and 290 tensors from Qwen2-0.5B-IMat-GGUF/Qwen2-0.5B.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              = qwen2
llama_model_loader: - kv   1:                               general.name str              = Qwen2-0.5B
llama_model_loader: - kv   2:                          qwen2.block_count u32              = 24
llama_model_loader: - kv   3:                       qwen2.context_length u32              = 131072
llama_model_loader: - kv   4:                     qwen2.embedding_length u32              = 896
llama_model_loader: - kv   5:                  qwen2.feed_forward_length u32              = 4864
llama_model_loader: - kv   6:                 qwen2.attention.head_count u32              = 14
llama_model_loader: - kv   7:              qwen2.attention.head_count_kv u32              = 2
llama_model_loader: - kv   8:                       qwen2.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv   9:     qwen2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  10:                          general.file_type u32              = 0
llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  12:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,151936]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  15:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 151643
llama_model_loader: - kv  17:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 151643
llama_model_loader: - kv  19:                    tokenizer.chat_template str              = {% for message in messages %}{% if lo...
llama_model_loader: - kv  20:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  290 tensors
llm_load_vocab: special tokens cache size = 293
llm_load_vocab: token to piece cache size = 0.9338 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = qwen2
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 151936
llm_load_print_meta: n_merges         = 151387
llm_load_print_meta: n_ctx_train      = 131072
llm_load_print_meta: n_embd           = 896
llm_load_print_meta: n_head           = 14
llm_load_print_meta: n_head_kv        = 2
llm_load_print_meta: n_layer          = 24
llm_load_print_meta: n_rot            = 64
llm_load_print_meta: n_embd_head_k    = 64
llm_load_print_meta: n_embd_head_v    = 64
llm_load_print_meta: n_gqa            = 7
llm_load_print_meta: n_embd_k_gqa     = 128
llm_load_print_meta: n_embd_v_gqa     = 128
llm_load_print_meta: f_norm_eps       = 0.0e+00
llm_load_print_meta: f_norm_rms_eps   = 1.0e-06
llm_load_print_meta: f_clamp_kqv      = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 4864
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 2
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 131072
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = 1B
llm_load_print_meta: model ftype      = all F32
llm_load_print_meta: model params     = 494.03 M
llm_load_print_meta: model size       = 1.84 GiB (32.00 BPW) 
llm_load_print_meta: general.name     = Qwen2-0.5B
llm_load_print_meta: BOS token        = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token        = 151643 '<|endoftext|>'
llm_load_print_meta: PAD token        = 151643 '<|endoftext|>'
llm_load_print_meta: LF token         = 148848 'ÄĬ'
llm_load_print_meta: EOT token        = 151645 '<|im_end|>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size =    0.28 MiB
llm_load_tensors: offloading 24 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 25/25 layers to GPU
llm_load_tensors:        CPU buffer size =   519.31 MiB
llm_load_tensors:      CUDA0 buffer size =  1884.59 MiB
...........................................................
llama_new_context_with_model: n_ctx      = 512
llama_new_context_with_model: n_batch    = 512
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      CUDA0 KV buffer size =     6.00 MiB
llama_new_context_with_model: KV self size  =    6.00 MiB, K (f16):    3.00 MiB, V (f16):    3.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.58 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =   298.50 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =     2.76 MiB
llama_new_context_with_model: graph nodes  = 846
llama_new_context_with_model: graph splits = 2

system_info: n_threads = 25 / 32 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | 
compute_imatrix: tokenizing the input ..
compute_imatrix: tokenization took 140.904 ms
compute_imatrix: computing over 128 chunks with batch_size 512
compute_imatrix: 0.28 seconds per pass - ETA 0.58 minutes
[1]10.1721,[2]8.3176,[3]7.5172,[4]9.2414,[5]8.9969,[6]8.1977,[7]8.8937,[8]9.2658,[9]9.9803,
save_imatrix: stored collected data after 10 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[10]9.2031,[11]8.8157,[12]9.5016,[13]10.4404,[14]10.6447,[15]11.3873,[16]11.8833,[17]11.9791,[18]12.5532,[19]12.0327,
save_imatrix: stored collected data after 20 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[20]12.0846,[21]12.1975,[22]12.2027,[23]12.0807,[24]12.4923,[25]12.7013,[26]12.6513,[27]13.0627,[28]13.4163,[29]13.8037,
save_imatrix: stored collected data after 30 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[30]13.7061,[31]13.1443,[32]12.5288,[33]12.1655,[34]11.9420,[35]11.6796,[36]11.7890,[37]12.0546,[38]12.2444,[39]12.2581,
save_imatrix: stored collected data after 40 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[40]12.4652,[41]12.4853,[42]13.0221,[43]13.5130,[44]13.9855,[45]14.3595,[46]14.5631,[47]14.3458,[48]14.3392,[49]14.4412,
save_imatrix: stored collected data after 50 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[50]14.4927,[51]14.3272,[52]14.3899,[53]14.6646,[54]14.8202,[55]15.0148,[56]15.0869,[57]15.0882,[58]15.1209,[59]15.1033,
save_imatrix: stored collected data after 60 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[60]15.1090,[61]14.9807,[62]14.9169,[63]14.9857,[64]15.0805,[65]14.9817,[66]14.9546,[67]14.9305,[68]14.8649,[69]14.8360,
save_imatrix: stored collected data after 70 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[70]14.8322,[71]14.7522,[72]14.6928,[73]14.7024,[74]14.5408,[75]14.4171,[76]14.3052,[77]14.2548,[78]14.2444,[79]14.1985,
save_imatrix: stored collected data after 80 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[80]14.1018,[81]14.1258,[82]14.0997,[83]13.9914,[84]14.0604,[85]14.0948,[86]13.9901,[87]13.9129,[88]13.8585,[89]13.8845,
save_imatrix: stored collected data after 90 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[90]13.8996,[91]13.8839,[92]13.7192,[93]13.5568,[94]13.3917,[95]13.2385,[96]13.0970,[97]12.9408,[98]12.7971,[99]12.7518,
save_imatrix: stored collected data after 100 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[100]12.7727,[101]12.8037,[102]12.9817,[103]13.1216,[104]13.2440,[105]13.4238,[106]13.5376,[107]13.5898,[108]13.5245,[109]13.5001,
save_imatrix: stored collected data after 110 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[110]13.5125,[111]13.4647,[112]13.4106,[113]13.4351,[114]13.4962,[115]13.4848,[116]13.4894,[117]13.4998,[118]13.5375,[119]13.5129,
save_imatrix: stored collected data after 120 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat
[120]13.4830,[121]13.4752,[122]13.3882,[123]13.4591,[124]13.5547,[125]13.6481,[126]13.7664,[127]13.8600,[128]13.9527,
save_imatrix: stored collected data after 128 chunks in Qwen2-0.5B-IMat-GGUF/imatrix.dat

llama_print_timings:        load time =     913.95 ms
llama_print_timings:      sample time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
llama_print_timings: prompt eval time =   16920.17 ms / 65536 tokens (    0.26 ms per token,  3873.25 tokens per second)
llama_print_timings:        eval time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
llama_print_timings:       total time =   18546.35 ms / 65537 tokens

Final estimate: PPL = 13.9527 +/- 0.21597