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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  = 1717697501
llama_model_loader: loaded meta data with 21 key-value pairs and 290 tensors from Qwen2-0.5B-Instruct-IMat-GGUF/Qwen2-0.5B-Instruct.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-Instruct
llama_model_loader: - kv   2:                          qwen2.block_count u32              = 24
llama_model_loader: - kv   3:                       qwen2.context_length u32              = 32768
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              = 151645
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      = 32768
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  = 32768
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-Instruct
llm_load_print_meta: BOS token        = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token        = 151645 '<|im_end|>'
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 143.46 ms
compute_imatrix: computing over 128 chunks with batch_size 512
compute_imatrix: 0.32 seconds per pass - ETA 0.68 minutes
[1]11.0153,[2]8.7867,[3]7.8438,[4]9.5496,[5]9.3272,[6]8.4608,[7]9.2335,[8]9.6817,[9]10.4469,
save_imatrix: stored collected data after 10 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[10]9.6325,[11]9.1945,[12]9.9034,[13]10.8543,[14]11.0685,[15]11.8463,[16]12.3478,[17]12.4503,[18]13.0519,[19]12.5067,
save_imatrix: stored collected data after 20 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[20]12.5579,[21]12.6907,[22]12.7226,[23]12.5846,[24]13.0383,[25]13.2262,[26]13.2079,[27]13.6637,[28]14.0292,[29]14.4747,
save_imatrix: stored collected data after 30 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[30]14.3979,[31]13.7993,[32]13.1442,[33]12.7399,[34]12.4904,[35]12.2049,[36]12.3080,[37]12.6005,[38]12.8030,[39]12.8264,
save_imatrix: stored collected data after 40 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[40]13.0478,[41]13.0751,[42]13.6493,[43]14.1599,[44]14.6647,[45]15.0588,[46]15.2725,[47]15.0340,[48]15.0376,[49]15.1503,
save_imatrix: stored collected data after 50 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[50]15.1859,[51]15.0006,[52]15.0761,[53]15.3538,[54]15.5192,[55]15.7389,[56]15.8111,[57]15.8110,[58]15.8510,[59]15.8512,
save_imatrix: stored collected data after 60 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[60]15.8503,[61]15.7101,[62]15.6407,[63]15.7145,[64]15.8063,[65]15.7094,[66]15.6743,[67]15.6515,[68]15.5856,[69]15.5664,
save_imatrix: stored collected data after 70 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[70]15.5587,[71]15.4788,[72]15.4158,[73]15.4249,[74]15.2522,[75]15.1291,[76]15.0129,[77]14.9624,[78]14.9531,[79]14.9062,
save_imatrix: stored collected data after 80 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[80]14.8102,[81]14.8351,[82]14.8070,[83]14.6925,[84]14.7681,[85]14.8005,[86]14.6922,[87]14.6163,[88]14.5540,[89]14.5760,
save_imatrix: stored collected data after 90 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[90]14.5867,[91]14.5719,[92]14.3912,[93]14.2202,[94]14.0447,[95]13.8821,[96]13.7340,[97]13.5649,[98]13.4127,[99]13.3627,
save_imatrix: stored collected data after 100 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[100]13.3850,[101]13.4151,[102]13.5983,[103]13.7427,[104]13.8673,[105]14.0567,[106]14.1836,[107]14.2397,[108]14.1732,[109]14.1509,
save_imatrix: stored collected data after 110 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[110]14.1576,[111]14.1077,[112]14.0437,[113]14.0704,[114]14.1317,[115]14.1182,[116]14.1220,[117]14.1334,[118]14.1737,[119]14.1457,
save_imatrix: stored collected data after 120 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat
[120]14.1093,[121]14.0986,[122]14.0078,[123]14.0817,[124]14.1812,[125]14.2818,[126]14.3994,[127]14.4974,[128]14.5959,
save_imatrix: stored collected data after 128 chunks in Qwen2-0.5B-Instruct-IMat-GGUF/imatrix.dat

llama_print_timings:        load time =     978.27 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 =   16847.44 ms / 65536 tokens (    0.26 ms per token,  3889.97 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 =   18657.16 ms / 65537 tokens

Final estimate: PPL = 14.5959 +/- 0.23103