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imatrix.log

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+llama_model_loader: loaded meta data with 28 key-value pairs and 464 tensors from Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/Gemma-2-9B-It-SPPO-Iter3.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-SPPO-Iter3
+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:              gemma2.attn_logit_softcapping f32              = 50.000000
+llama_model_loader: - kv  13:             gemma2.final_logit_softcapping f32              = 30.000000
+llama_model_loader: - kv  14:                       tokenizer.ggml.model str              = llama
+llama_model_loader: - kv  15:                         tokenizer.ggml.pre str              = default
+llama_model_loader: - kv  16:                      tokenizer.ggml.tokens arr[str,256000]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
+llama_model_loader: - kv  17:                      tokenizer.ggml.scores arr[f32,256000]  = [-1000.000000, -1000.000000, -1000.00...
+llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, ...
+llama_model_loader: - kv  19:                tokenizer.ggml.bos_token_id u32              = 2
+llama_model_loader: - kv  20:                tokenizer.ggml.eos_token_id u32              = 1
+llama_model_loader: - kv  21:            tokenizer.ggml.unknown_token_id u32              = 3
+llama_model_loader: - kv  22:            tokenizer.ggml.padding_token_id u32              = 0
+llama_model_loader: - kv  23:               tokenizer.ggml.add_bos_token bool             = true
+llama_model_loader: - kv  24:               tokenizer.ggml.add_eos_token bool             = false
+llama_model_loader: - kv  25:                    tokenizer.chat_template str              = {{ bos_token }}{% if messages[0]['rol...
+llama_model_loader: - kv  26:            tokenizer.ggml.add_space_prefix bool             = false
+llama_model_loader: - kv  27:               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 = 261
+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       = 9B
+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-SPPO-Iter3
+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  = 1690
+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 93.278 ms
+compute_imatrix: computing over 128 chunks with batch_size 512
+compute_imatrix: 0.84 seconds per pass - ETA 1.78 minutes
+[1]16.6552,[2]8.0903,[3]6.9962,[4]8.3971,[5]9.2464,[6]9.7300,[7]10.7022,[8]11.6187,[9]11.9992,
+save_imatrix: stored collected data after 10 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[10]10.4973,[11]10.2809,[12]11.3377,[13]11.9631,[14]12.0849,[15]12.9207,[16]13.0206,[17]13.0855,[18]13.5418,[19]13.4242,
+save_imatrix: stored collected data after 20 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[20]13.7130,[21]14.8192,[22]14.8997,[23]14.7292,[24]14.9818,[25]14.8718,[26]14.5962,[27]14.8364,[28]15.0628,[29]15.0534,
+save_imatrix: stored collected data after 30 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[30]15.2443,[31]14.0970,[32]13.4552,[33]13.0165,[34]12.6321,[35]12.3731,[36]12.5212,[37]12.8425,[38]13.0140,[39]13.1960,
+save_imatrix: stored collected data after 40 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[40]13.3016,[41]13.3337,[42]13.9154,[43]14.2830,[44]14.6994,[45]14.9396,[46]14.6625,[47]14.4206,[48]14.6417,[49]14.8669,
+save_imatrix: stored collected data after 50 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[50]14.6774,[51]14.5402,[52]14.6070,[53]14.8337,[54]15.1521,[55]15.4017,[56]15.5381,[57]15.5412,[58]15.5433,[59]15.3203,
+save_imatrix: stored collected data after 60 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[60]15.1412,[61]14.9436,[62]14.7716,[63]14.8956,[64]15.0538,[65]14.9025,[66]14.9251,[67]14.8897,[68]14.8316,[69]14.7527,
+save_imatrix: stored collected data after 70 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[70]14.6922,[71]14.6700,[72]14.6271,[73]14.6927,[74]14.6179,[75]14.4837,[76]14.4578,[77]14.4619,[78]14.4124,[79]14.3156,
+save_imatrix: stored collected data after 80 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[80]14.3808,[81]14.4619,[82]14.4841,[83]14.5843,[84]14.6263,[85]14.3919,[86]14.3208,[87]14.1543,[88]14.1997,[89]14.1722,
+save_imatrix: stored collected data after 90 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[90]14.2455,[91]14.1862,[92]14.0935,[93]14.0125,[94]13.8942,[95]13.8216,[96]13.7275,[97]13.6539,[98]13.5585,[99]13.6079,
+save_imatrix: stored collected data after 100 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[100]13.6242,[101]13.7690,[102]13.8623,[103]13.9214,[104]14.1043,[105]14.2414,[106]14.2558,[107]14.2652,[108]14.1996,[109]14.2351,
+save_imatrix: stored collected data after 110 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[110]14.1081,[111]13.9695,[112]13.7990,[113]13.8842,[114]13.9366,[115]13.9254,[116]13.8930,[117]13.9520,[118]13.9874,[119]14.0052,
+save_imatrix: stored collected data after 120 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[120]13.9951,[121]14.0036,[122]13.9634,[123]13.9903,[124]14.0884,[125]14.1808,[126]14.2925,[127]14.3374,[128]14.3945,
+save_imatrix: stored collected data after 128 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+
+llama_print_timings:        load time =    6383.63 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 =   91484.26 ms / 65536 tokens (    1.40 ms per token,   716.36 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 =   98451.28 ms / 65537 tokens
+
+Final estimate: PPL = 14.3945 +/- 0.28244