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llama_model_loader: loaded meta data with 25 key-value pairs and 273 tensors from NuminaMath-7B-TIR-IMat-GGUF/NuminaMath-7B-TIR.Q8_0.gguf.hardlink.gguf (version GGUF V3 (latest)) |
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llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. |
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llama_model_loader: - kv 0: general.architecture str = llama |
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llama_model_loader: - kv 1: general.name str = NuminaMath-7B-TIR |
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llama_model_loader: - kv 2: llama.block_count u32 = 30 |
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llama_model_loader: - kv 3: llama.context_length u32 = 4096 |
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llama_model_loader: - kv 4: llama.embedding_length u32 = 4096 |
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llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008 |
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llama_model_loader: - kv 6: llama.attention.head_count u32 = 32 |
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llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 32 |
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llama_model_loader: - kv 8: llama.rope.freq_base f32 = 10000.000000 |
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llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001 |
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llama_model_loader: - kv 10: general.file_type u32 = 7 |
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llama_model_loader: - kv 11: llama.vocab_size u32 = 102400 |
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llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128 |
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llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 |
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llama_model_loader: - kv 14: tokenizer.ggml.pre str = deepseek-llm |
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llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,102400] = ["!", "\"", "#", "$", "%", "&", "'", ... |
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llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,102400] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... |
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llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,99757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e... |
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llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 100000 |
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llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 100001 |
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llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 100001 |
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llama_model_loader: - kv 21: tokenizer.ggml.add_bos_token bool = true |
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llama_model_loader: - kv 22: tokenizer.ggml.add_eos_token bool = false |
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llama_model_loader: - kv 23: tokenizer.chat_template str = {% for message in messages %}{% if (m... |
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llama_model_loader: - kv 24: general.quantization_version u32 = 2 |
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llama_model_loader: - type f32: 61 tensors |
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llama_model_loader: - type q8_0: 212 tensors |
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llm_load_vocab: special tokens cache size = 2400 |
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llm_load_vocab: token to piece cache size = 0.6659 MB |
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llm_load_print_meta: format = GGUF V3 (latest) |
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llm_load_print_meta: arch = llama |
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llm_load_print_meta: vocab type = BPE |
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llm_load_print_meta: n_vocab = 102400 |
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llm_load_print_meta: n_merges = 99757 |
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llm_load_print_meta: vocab_only = 0 |
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llm_load_print_meta: n_ctx_train = 4096 |
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llm_load_print_meta: n_embd = 4096 |
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llm_load_print_meta: n_layer = 30 |
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llm_load_print_meta: n_head = 32 |
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llm_load_print_meta: n_head_kv = 32 |
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llm_load_print_meta: n_rot = 128 |
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llm_load_print_meta: n_swa = 0 |
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llm_load_print_meta: n_embd_head_k = 128 |
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llm_load_print_meta: n_embd_head_v = 128 |
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llm_load_print_meta: n_gqa = 1 |
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llm_load_print_meta: n_embd_k_gqa = 4096 |
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llm_load_print_meta: n_embd_v_gqa = 4096 |
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llm_load_print_meta: f_norm_eps = 0.0e+00 |
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llm_load_print_meta: f_norm_rms_eps = 1.0e-06 |
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llm_load_print_meta: f_clamp_kqv = 0.0e+00 |
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llm_load_print_meta: f_max_alibi_bias = 0.0e+00 |
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llm_load_print_meta: f_logit_scale = 0.0e+00 |
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llm_load_print_meta: n_ff = 11008 |
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llm_load_print_meta: n_expert = 0 |
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llm_load_print_meta: n_expert_used = 0 |
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llm_load_print_meta: causal attn = 1 |
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llm_load_print_meta: pooling type = 0 |
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llm_load_print_meta: rope type = 0 |
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llm_load_print_meta: rope scaling = linear |
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llm_load_print_meta: freq_base_train = 10000.0 |
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llm_load_print_meta: freq_scale_train = 1 |
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llm_load_print_meta: n_ctx_orig_yarn = 4096 |
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llm_load_print_meta: rope_finetuned = unknown |
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llm_load_print_meta: ssm_d_conv = 0 |
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llm_load_print_meta: ssm_d_inner = 0 |
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llm_load_print_meta: ssm_d_state = 0 |
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llm_load_print_meta: ssm_dt_rank = 0 |
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llm_load_print_meta: model type = ?B |
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llm_load_print_meta: model ftype = Q8_0 |
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llm_load_print_meta: model params = 6.91 B |
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llm_load_print_meta: model size = 6.84 GiB (8.50 BPW) |
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llm_load_print_meta: general.name = NuminaMath-7B-TIR |
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llm_load_print_meta: BOS token = 100000 '<｜begin▁of▁sentence｜>' |
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llm_load_print_meta: EOS token = 100001 '<｜end▁of▁sentence｜>' |
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llm_load_print_meta: PAD token = 100001 '<｜end▁of▁sentence｜>' |
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llm_load_print_meta: LF token = 126 'Ä' |
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llm_load_print_meta: max token length = 256 |
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ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no |
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ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no |
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ggml_cuda_init: found 1 CUDA devices: |
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Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes |
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llm_load_tensors: ggml ctx size = 0.26 MiB |
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llm_load_tensors: offloading 30 repeating layers to GPU |
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llm_load_tensors: offloading non-repeating layers to GPU |
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llm_load_tensors: offloaded 31/31 layers to GPU |
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llm_load_tensors: CPU buffer size = 425.00 MiB |
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llm_load_tensors: CUDA0 buffer size = 6577.84 MiB |
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.......................................................................................... |
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llama_new_context_with_model: n_ctx = 512 |
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llama_new_context_with_model: n_batch = 512 |
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llama_new_context_with_model: n_ubatch = 512 |
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llama_new_context_with_model: flash_attn = 0 |
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llama_new_context_with_model: freq_base = 10000.0 |
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llama_new_context_with_model: freq_scale = 1 |
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llama_kv_cache_init: CUDA0 KV buffer size = 240.00 MiB |
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llama_new_context_with_model: KV self size = 240.00 MiB, K (f16): 120.00 MiB, V (f16): 120.00 MiB |
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llama_new_context_with_model: CUDA_Host output buffer size = 0.39 MiB |
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llama_new_context_with_model: CUDA0 compute buffer size = 208.00 MiB |
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llama_new_context_with_model: CUDA_Host compute buffer size = 9.01 MiB |
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llama_new_context_with_model: graph nodes = 966 |
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llama_new_context_with_model: graph splits = 2 |
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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 | |
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compute_imatrix: tokenizing the input .. |
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compute_imatrix: tokenization took 219.795 ms |
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compute_imatrix: computing over 139 chunks with batch_size 512 |
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compute_imatrix: 0.66 seconds per pass - ETA 1.53 minutes |
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[1]10.4314,[2]7.3682,[3]6.7831,[4]8.2509,[5]7.7539,[6]7.2769,[7]8.2499,[8]8.2793,[9]9.5899, |
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save_imatrix: stored collected data after 10 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[10]9.8051,[11]9.0158,[12]9.8822,[13]10.7192,[14]11.4750,[15]11.7074,[16]12.4342,[17]12.8366,[18]13.0696,[19]13.6454, |
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save_imatrix: stored collected data after 20 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[20]12.7369,[21]12.7164,[22]13.0572,[23]13.4045,[24]13.0591,[25]13.4993,[26]13.0929,[27]13.5825,[28]13.4809,[29]13.9218, |
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save_imatrix: stored collected data after 30 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[30]14.3508,[31]14.8240,[32]14.6801,[33]14.0268,[34]13.0593,[35]12.3150,[36]12.1439,[37]12.1304,[38]12.0899,[39]11.8205, |
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save_imatrix: stored collected data after 40 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[40]11.7968,[41]11.4809,[42]11.2521,[43]11.4326,[44]11.5349,[45]11.8091,[46]11.8210,[47]12.4688,[48]12.9043,[49]13.2747, |
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save_imatrix: stored collected data after 50 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[50]13.5610,[51]13.7512,[52]13.5889,[53]13.7792,[54]14.0260,[55]14.1128,[56]13.9351,[57]13.8070,[58]13.7664,[59]13.9588, |
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save_imatrix: stored collected data after 60 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[60]14.1955,[61]14.4787,[62]14.5778,[63]14.6177,[64]14.6769,[65]14.6685,[66]14.6656,[67]14.6345,[68]14.5578,[69]14.6843, |
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save_imatrix: stored collected data after 70 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[70]14.8954,[71]14.8467,[72]14.8260,[73]14.7263,[74]14.6219,[75]14.4769,[76]14.3974,[77]14.3304,[78]14.2765,[79]14.1135, |
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save_imatrix: stored collected data after 80 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[80]14.0793,[81]14.0205,[82]13.9517,[83]13.8239,[84]13.7387,[85]13.6672,[86]13.5425,[87]13.4674,[88]13.4413,[89]13.4553, |
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save_imatrix: stored collected data after 90 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[90]13.3877,[91]13.4239,[92]13.4322,[93]13.3130,[94]13.2679,[95]13.2266,[96]13.3477,[97]13.4149,[98]13.4163,[99]13.2541, |
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save_imatrix: stored collected data after 100 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[100]13.0952,[101]12.9394,[102]12.7769,[103]12.6021,[104]12.4604,[105]12.3279,[106]12.1804,[107]12.0326,[108]11.9975,[109]12.0266, |
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save_imatrix: stored collected data after 110 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[110]12.0979,[111]12.2131,[112]12.3274,[113]12.4241,[114]12.6192,[115]12.7171,[116]12.7818,[117]12.7623,[118]12.8884,[119]12.8693, |
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save_imatrix: stored collected data after 120 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[120]12.8420,[121]12.7717,[122]12.7185,[123]12.8016,[124]12.8760,[125]12.8461,[126]12.8485,[127]12.8573,[128]12.9072,[129]12.9172, |
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save_imatrix: stored collected data after 130 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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[130]12.9305,[131]12.9718,[132]12.9349,[133]12.8673,[134]12.9903,[135]13.1251,[136]13.2243,[137]13.3934,[138]13.5698,[139]13.6705, |
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save_imatrix: stored collected data after 139 chunks in NuminaMath-7B-TIR-IMat-GGUF/imatrix.dat |
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llama_print_timings: load time = 9195.41 ms |
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llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) |
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llama_print_timings: prompt eval time = 71105.13 ms / 71168 tokens ( 1.00 ms per token, 1000.88 tokens per second) |
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llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) |
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llama_print_timings: total time = 80417.25 ms / 71169 tokens |
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Final estimate: PPL = 13.6705 +/- 0.25616 |
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