Upload imatrix.log with huggingface_hub

imatrix.log

@@ -0,0 +1,143 @@
+llama_model_loader: loaded meta data with 25 key-value pairs and 464 tensors from gemma-2-9b-it-IMat-GGUF/gemma-2-9b-it.Q8_0.gguf.hardlink.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              = gemma2
+llama_model_loader: - kv   1:                               general.name str              = gemma-2-9b-it
+llama_model_loader: - kv   2:                      gemma2.context_length u32              = 8192
+llama_model_loader: - kv   3:                    gemma2.embedding_length u32              = 3584
+llama_model_loader: - kv   4:                         gemma2.block_count u32              = 42
+llama_model_loader: - kv   5:                 gemma2.feed_forward_length u32              = 14336
+llama_model_loader: - kv   6:                gemma2.attention.head_count u32              = 16
+llama_model_loader: - kv   7:             gemma2.attention.head_count_kv u32              = 8
+llama_model_loader: - kv   8:    gemma2.attention.layer_norm_rms_epsilon f32              = 0.000001
+llama_model_loader: - kv   9:                gemma2.attention.key_length u32              = 256
+llama_model_loader: - kv  10:              gemma2.attention.value_length u32              = 256
+llama_model_loader: - kv  11:                          general.file_type u32              = 7
+llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
+llama_model_loader: - kv  13:                         tokenizer.ggml.pre str              = default
+llama_model_loader: - kv  14:                      tokenizer.ggml.tokens arr[str,256000]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
+llama_model_loader: - kv  15:                      tokenizer.ggml.scores arr[f32,256000]  = [0.000000, 0.000000, 0.000000, 0.0000...
+llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
+llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 2
+llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 1
+llama_model_loader: - kv  19:            tokenizer.ggml.unknown_token_id u32              = 3
+llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 0
+llama_model_loader: - kv  21:               tokenizer.ggml.add_bos_token bool             = true
+llama_model_loader: - kv  22:               tokenizer.ggml.add_eos_token bool             = false
+llama_model_loader: - kv  23:                    tokenizer.chat_template str              = {{ bos_token }}{% if messages[0]['rol...
+llama_model_loader: - kv  24:               general.quantization_version u32              = 2
+llama_model_loader: - type  f32:  169 tensors
+llama_model_loader: - type q8_0:  295 tensors
+llm_load_vocab: special tokens cache size = 260
+llm_load_vocab: token to piece cache size = 1.6014 MB
+llm_load_print_meta: format           = GGUF V3 (latest)
+llm_load_print_meta: arch             = gemma2
+llm_load_print_meta: vocab type       = SPM
+llm_load_print_meta: n_vocab          = 256000
+llm_load_print_meta: n_merges         = 0
+llm_load_print_meta: n_ctx_train      = 8192
+llm_load_print_meta: n_embd           = 3584
+llm_load_print_meta: n_head           = 16
+llm_load_print_meta: n_head_kv        = 8
+llm_load_print_meta: n_layer          = 42
+llm_load_print_meta: n_rot            = 224
+llm_load_print_meta: n_embd_head_k    = 256
+llm_load_print_meta: n_embd_head_v    = 256
+llm_load_print_meta: n_gqa            = 2
+llm_load_print_meta: n_embd_k_gqa     = 2048
+llm_load_print_meta: n_embd_v_gqa     = 2048
+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             = 14336
+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  = 10000.0
+llm_load_print_meta: freq_scale_train = 1
+llm_load_print_meta: n_ctx_orig_yarn  = 8192
+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       = ?B
+llm_load_print_meta: model ftype      = Q8_0
+llm_load_print_meta: model params     = 9.24 B
+llm_load_print_meta: model size       = 9.15 GiB (8.50 BPW) 
+llm_load_print_meta: general.name     = gemma-2-9b-it
+llm_load_print_meta: BOS token        = 2 '<bos>'
+llm_load_print_meta: EOS token        = 1 '<eos>'
+llm_load_print_meta: UNK token        = 3 '<unk>'
+llm_load_print_meta: PAD token        = 0 '<pad>'
+llm_load_print_meta: LF token         = 227 '<0x0A>'
+llm_load_print_meta: EOT token        = 107 '<end_of_turn>'
+llm_load_print_meta: max token length = 93
+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 4090, compute capability 8.9, VMM: yes
+llm_load_tensors: ggml ctx size =    0.41 MiB
+llm_load_tensors: offloading 42 repeating layers to GPU
+llm_load_tensors: offloading non-repeating layers to GPU
+llm_load_tensors: offloaded 43/43 layers to GPU
+llm_load_tensors:        CPU buffer size =   929.69 MiB
+llm_load_tensors:      CUDA0 buffer size =  9366.12 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  = 10000.0
+llama_new_context_with_model: freq_scale = 1
+llama_kv_cache_init:      CUDA0 KV buffer size =   168.00 MiB
+llama_new_context_with_model: KV self size  =  168.00 MiB, K (f16):   84.00 MiB, V (f16):   84.00 MiB
+llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB
+llama_new_context_with_model:      CUDA0 compute buffer size =   507.00 MiB
+llama_new_context_with_model:  CUDA_Host compute buffer size =     8.01 MiB
+llama_new_context_with_model: graph nodes  = 1561
+llama_new_context_with_model: graph splits = 2
+
+system_info: n_threads = 25 / 32 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | 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 124.683 ms
+compute_imatrix: computing over 128 chunks with batch_size 512
+compute_imatrix: 0.83 seconds per pass - ETA 1.75 minutes
+[1]11.6771,[2]7.4833,[3]6.4754,[4]8.4522,[5]8.6895,[6]7.0038,[7]7.8546,[8]8.4646,[9]8.8183,
+save_imatrix: stored collected data after 10 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[10]7.6272,[11]7.8174,[12]8.7305,[13]9.5990,[14]9.9023,[15]10.8211,[16]11.2734,[17]11.4621,[18]12.0211,[19]11.4085,
+save_imatrix: stored collected data after 20 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[20]11.7441,[21]12.0377,[22]12.0034,[23]12.2039,[24]12.3667,[25]12.6682,[26]12.2060,[27]12.5602,[28]12.8297,[29]12.6891,
+save_imatrix: stored collected data after 30 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[30]12.5921,[31]11.7099,[32]11.3067,[33]11.1819,[34]10.9639,[35]10.8633,[36]10.8581,[37]10.8758,[38]11.0319,[39]11.3075,
+save_imatrix: stored collected data after 40 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[40]11.5737,[41]11.7938,[42]12.2299,[43]12.6891,[44]13.0918,[45]13.3168,[46]13.0891,[47]13.1461,[48]13.4924,[49]13.7363,
+save_imatrix: stored collected data after 50 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[50]13.3847,[51]13.4436,[52]13.5379,[53]13.7711,[54]14.1253,[55]14.3239,[56]14.4230,[57]14.4063,[58]14.4329,[59]14.1763,
+save_imatrix: stored collected data after 60 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[60]13.9614,[61]13.7580,[62]13.6845,[63]13.7762,[64]13.7836,[65]13.7635,[66]13.8324,[67]13.7439,[68]13.6359,[69]13.6722,
+save_imatrix: stored collected data after 70 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[70]13.6115,[71]13.5899,[72]13.6093,[73]13.5783,[74]13.4840,[75]13.4323,[76]13.4368,[77]13.4732,[78]13.4611,[79]13.3709,
+save_imatrix: stored collected data after 80 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[80]13.4802,[81]13.5753,[82]13.5417,[83]13.5532,[84]13.6483,[85]13.4302,[86]13.3705,[87]13.2581,[88]13.2679,[89]13.3038,
+save_imatrix: stored collected data after 90 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[90]13.3596,[91]13.2432,[92]13.1122,[93]12.9672,[94]12.8206,[95]12.7243,[96]12.5957,[97]12.4788,[98]12.3693,[99]12.4411,
+save_imatrix: stored collected data after 100 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[100]12.4826,[101]12.6258,[102]12.7398,[103]12.8584,[104]13.1219,[105]13.3135,[106]13.3536,[107]13.4055,[108]13.4438,[109]13.3964,
+save_imatrix: stored collected data after 110 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[110]13.3558,[111]13.2382,[112]13.1135,[113]13.1991,[114]13.2304,[115]13.2366,[116]13.2327,[117]13.3196,[118]13.3490,[119]13.3551,
+save_imatrix: stored collected data after 120 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+[120]13.3762,[121]13.4465,[122]13.3793,[123]13.4577,[124]13.5307,[125]13.5815,[126]13.6899,[127]13.7724,[128]13.8487,
+save_imatrix: stored collected data after 128 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
+
+llama_print_timings:        load time =    2452.80 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 =   91799.71 ms / 65536 tokens (    1.40 ms per token,   713.90 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 =   94944.80 ms / 65537 tokens
+
+Final estimate: PPL = 13.8487 +/- 0.28454