File size: 12,728 Bytes
a39d942 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
main: build = 3010 (95f84d5c)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1716911218
llama_model_loader: loaded meta data with 27 key-value pairs and 561 tensors from AutoCoder-IMat-GGUF/AutoCoder.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 = llama
llama_model_loader: - kv 1: general.name str = AutoCoder
llama_model_loader: - kv 2: llama.block_count u32 = 62
llama_model_loader: - kv 3: llama.context_length u32 = 16384
llama_model_loader: - kv 4: llama.embedding_length u32 = 7168
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 19200
llama_model_loader: - kv 6: llama.attention.head_count u32 = 56
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 100000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 0
llama_model_loader: - kv 11: llama.vocab_size u32 = 32256
llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 13: llama.rope.scaling.type str = linear
llama_model_loader: - kv 14: llama.rope.scaling.factor f32 = 4.000000
llama_model_loader: - kv 15: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 16: tokenizer.ggml.pre str = deepseek-coder
llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,32256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,32256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: tokenizer.ggml.merges arr[str,31757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e...
llama_model_loader: - kv 20: tokenizer.ggml.bos_token_id u32 = 32013
llama_model_loader: - kv 21: tokenizer.ggml.eos_token_id u32 = 32021
llama_model_loader: - kv 22: tokenizer.ggml.padding_token_id u32 = 32014
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 = {% if messages[0]['role'] == 'system'...
llama_model_loader: - kv 26: general.quantization_version u32 = 2
llama_model_loader: - type f32: 561 tensors
llm_load_vocab: mismatch in special tokens definition ( 243/32256 vs 256/32256 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 32256
llm_load_print_meta: n_merges = 31757
llm_load_print_meta: n_ctx_train = 16384
llm_load_print_meta: n_embd = 7168
llm_load_print_meta: n_head = 56
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 62
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 7
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
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 = 19200
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 = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 100000.0
llm_load_print_meta: freq_scale_train = 0.25
llm_load_print_meta: n_yarn_orig_ctx = 16384
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 = all F32
llm_load_print_meta: model params = 33.34 B
llm_load_print_meta: model size = 124.21 GiB (32.00 BPW)
llm_load_print_meta: general.name = AutoCoder
llm_load_print_meta: BOS token = 32013 '<｜begin▁of▁sentence｜>'
llm_load_print_meta: EOS token = 32021 '<|EOT|>'
llm_load_print_meta: PAD token = 32014 '<｜end▁of▁sentence｜>'
llm_load_print_meta: LF token = 126 'Ä'
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 = 0.57 MiB
llm_load_tensors: offloading 10 repeating layers to GPU
llm_load_tensors: offloaded 10/63 layers to GPU
llm_load_tensors: CPU buffer size = 127193.42 MiB
llm_load_tensors: CUDA0 buffer size = 20230.55 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 = 100000.0
llama_new_context_with_model: freq_scale = 0.25
llama_kv_cache_init: CUDA_Host KV buffer size = 104.00 MiB
llama_kv_cache_init: CUDA0 KV buffer size = 20.00 MiB
llama_new_context_with_model: KV self size = 124.00 MiB, K (f16): 62.00 MiB, V (f16): 62.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.12 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 959.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 15.01 MiB
llama_new_context_with_model: graph nodes = 1990
llama_new_context_with_model: graph splits = 576
system_info: n_threads = 32 / 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 235.987 ms
compute_imatrix: computing over 236 chunks with batch_size 512
compute_imatrix: 85.02 seconds per pass - ETA 5 hours 34.40 minutes
[1]6.2367,[2]5.0981,[3]5.3674,[4]6.3093,[5]6.6962,[6]6.5139,[7]5.6034,[8]6.3346,[9]6.2327,
save_imatrix: stored collected data after 10 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[10]7.0182,[11]7.3080,[12]7.1931,[13]7.8130,[14]7.1667,[15]7.8930,[16]8.0319,[17]8.4303,[18]8.5621,[19]8.8975,
save_imatrix: stored collected data after 20 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[20]8.7195,[21]9.0143,[22]8.8119,[23]8.3915,[24]8.5450,[25]7.9526,[26]7.5086,[27]7.1777,[28]7.0598,[29]7.1306,
save_imatrix: stored collected data after 30 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[30]7.2243,[31]7.3420,[32]7.5052,[33]7.7138,[34]7.5603,[35]7.1611,[36]6.8229,[37]6.7698,[38]6.7691,[39]6.7560,
save_imatrix: stored collected data after 40 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[40]6.7272,[41]6.8435,[42]7.0138,[43]7.1658,[44]7.3762,[45]7.3547,[46]7.4967,[47]7.7038,[48]7.9130,[49]8.1219,
save_imatrix: stored collected data after 50 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[50]8.2618,[51]8.1807,[52]8.0399,[53]7.8965,[54]7.7444,[55]7.9113,[56]8.0362,[57]8.1050,[58]8.2624,[59]8.3243,
save_imatrix: stored collected data after 60 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[60]8.4889,[61]8.6123,[62]8.7770,[63]8.8948,[64]8.9956,[65]9.0936,[66]9.1913,[67]9.3509,[68]9.4569,[69]9.5059,
save_imatrix: stored collected data after 70 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[70]9.5613,[71]9.4594,[72]9.3993,[73]9.3739,[74]9.3351,[75]9.3279,[76]9.2996,[77]9.2548,[78]9.1472,[79]9.1006,
save_imatrix: stored collected data after 80 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[80]9.1075,[81]9.0669,[82]9.1354,[83]9.1885,[84]9.2184,[85]9.0658,[86]9.0755,[87]8.9919,[88]8.8167,[89]8.8220,
save_imatrix: stored collected data after 90 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[90]8.8249,[91]8.8873,[92]8.9070,[93]8.9475,[94]8.9910,[95]8.9723,[96]8.9361,[97]8.9400,[98]8.9640,[99]9.0133,
save_imatrix: stored collected data after 100 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[100]9.0484,[101]9.0443,[102]9.0419,[103]9.0161,[104]9.0064,[105]9.0092,[106]8.9743,[107]8.9700,[108]8.9735,[109]8.9437,
save_imatrix: stored collected data after 110 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[110]8.9297,[111]8.9006,[112]8.8987,[113]8.8891,[114]8.8714,[115]8.8467,[116]8.8307,[117]8.8270,[118]8.8133,[119]8.7494,
save_imatrix: stored collected data after 120 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[120]8.7974,[121]8.8334,[122]8.8401,[123]8.8185,[124]8.8434,[125]8.8608,[126]8.8472,[127]8.7689,[128]8.7780,[129]8.7923,
save_imatrix: stored collected data after 130 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[130]8.7391,[131]8.7488,[132]8.6890,[133]8.6261,[134]8.5618,[135]8.4958,[136]8.4314,[137]8.3654,[138]8.3062,[139]8.2436,
save_imatrix: stored collected data after 140 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[140]8.1961,[141]8.1307,[142]8.0784,[143]8.0174,[144]7.9439,[145]7.8924,[146]7.8435,[147]7.7859,[148]7.7273,[149]7.6772,
save_imatrix: stored collected data after 150 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[150]7.6288,[151]7.5735,[152]7.5221,[153]7.4689,[154]7.4131,[155]7.3718,[156]7.3188,[157]7.2882,[158]7.2251,[159]7.1737,
save_imatrix: stored collected data after 160 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[160]7.1659,[161]7.2120,[162]7.2339,[163]7.2855,[164]7.3378,[165]7.3149,[166]7.3304,[167]7.3299,[168]7.3212,[169]7.3309,
save_imatrix: stored collected data after 170 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[170]7.3366,[171]7.3420,[172]7.3335,[173]7.3637,[174]7.3576,[175]7.3754,[176]7.3666,[177]7.3789,[178]7.3875,[179]7.3976,
save_imatrix: stored collected data after 180 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[180]7.3970,[181]7.4175,[182]7.4364,[183]7.4421,[184]7.4629,[185]7.4892,[186]7.5225,[187]7.5317,[188]7.5587,[189]7.5713,
save_imatrix: stored collected data after 190 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[190]7.5875,[191]7.6106,[192]7.6421,[193]7.6671,[194]7.6765,[195]7.7298,[196]7.7414,[197]7.7303,[198]7.7852,[199]7.8435,
save_imatrix: stored collected data after 200 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[200]7.8993,[201]7.9646,[202]8.0040,[203]8.0264,[204]8.0384,[205]8.0071,[206]8.0052,[207]8.0328,[208]8.0716,[209]8.0787,
save_imatrix: stored collected data after 210 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[210]8.0894,[211]8.1033,[212]8.1210,[213]8.1402,[214]8.1424,[215]8.1519,[216]8.1691,[217]8.1997,[218]8.2578,[219]8.2313,
save_imatrix: stored collected data after 220 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[220]8.2453,[221]8.2315,[222]8.2474,[223]8.2451,[224]8.2428,[225]8.2667,[226]8.2477,[227]8.2620,[228]8.2658,[229]8.3247,
save_imatrix: stored collected data after 230 chunks in AutoCoder-IMat-GGUF/imatrix.dat
[230]8.3919,[231]8.4580,[232]8.5224,[233]8.5653,[234]8.5411,[235]8.5097,[236]8.4690,
save_imatrix: stored collected data after 236 chunks in AutoCoder-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 202649.20 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 = 2051921.29 ms / 120832 tokens ( 16.98 ms per token, 58.89 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 = 2170559.57 ms / 120833 tokens
Final estimate: PPL = 8.4690 +/- 0.09498
|