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imatrix.log

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+llama_model_loader: loaded meta data with 30 key-value pairs and 435 tensors from internlm2_5-20b-chat-IMat-GGUF/internlm2_5-20b-chat.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              = internlm2
+llama_model_loader: - kv   1:                               general.type str              = model
+llama_model_loader: - kv   2:                               general.name str              = Internlm2_5 20b Chat
+llama_model_loader: - kv   3:                           general.finetune str              = chat
+llama_model_loader: - kv   4:                           general.basename str              = internlm2_5
+llama_model_loader: - kv   5:                         general.size_label str              = 20B
+llama_model_loader: - kv   6:                            general.license str              = other
+llama_model_loader: - kv   7:                               general.tags arr[str,1]       = ["text-generation"]
+llama_model_loader: - kv   8:                   internlm2.context_length u32              = 32768
+llama_model_loader: - kv   9:                      internlm2.block_count u32              = 48
+llama_model_loader: - kv  10:                 internlm2.embedding_length u32              = 6144
+llama_model_loader: - kv  11:              internlm2.feed_forward_length u32              = 16384
+llama_model_loader: - kv  12:                   internlm2.rope.freq_base f32              = 50000000.000000
+llama_model_loader: - kv  13:             internlm2.attention.head_count u32              = 48
+llama_model_loader: - kv  14: internlm2.attention.layer_norm_rms_epsilon f32              = 0.000010
+llama_model_loader: - kv  15:          internlm2.attention.head_count_kv u32              = 8
+llama_model_loader: - kv  16:                          general.file_type u32              = 7
+llama_model_loader: - kv  17:                       tokenizer.ggml.model str              = llama
+llama_model_loader: - kv  18:                         tokenizer.ggml.pre str              = default
+llama_model_loader: - kv  19:                      tokenizer.ggml.tokens arr[str,92544]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
+llama_model_loader: - kv  20:                      tokenizer.ggml.scores arr[f32,92544]   = [-1000.000000, -1000.000000, -1000.00...
+llama_model_loader: - kv  21:                  tokenizer.ggml.token_type arr[i32,92544]   = [3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
+llama_model_loader: - kv  22:            tokenizer.ggml.add_space_prefix bool             = false
+llama_model_loader: - kv  23:                tokenizer.ggml.bos_token_id u32              = 1
+llama_model_loader: - kv  24:                tokenizer.ggml.eos_token_id u32              = 92542
+llama_model_loader: - kv  25:            tokenizer.ggml.padding_token_id u32              = 2
+llama_model_loader: - kv  26:               tokenizer.ggml.add_bos_token bool             = true
+llama_model_loader: - kv  27:               tokenizer.ggml.add_eos_token bool             = false
+llama_model_loader: - kv  28:                    tokenizer.chat_template str              = {{ bos_token }}{% for message in mess...
+llama_model_loader: - kv  29:               general.quantization_version u32              = 2
+llama_model_loader: - type  f32:   97 tensors
+llama_model_loader: - type q8_0:  338 tensors
+llm_load_vocab: special tokens cache size = 9
+llm_load_vocab: token to piece cache size = 0.5508 MB
+llm_load_print_meta: format           = GGUF V3 (latest)
+llm_load_print_meta: arch             = internlm2
+llm_load_print_meta: vocab type       = SPM
+llm_load_print_meta: n_vocab          = 92544
+llm_load_print_meta: n_merges         = 0
+llm_load_print_meta: vocab_only       = 0
+llm_load_print_meta: n_ctx_train      = 32768
+llm_load_print_meta: n_embd           = 6144
+llm_load_print_meta: n_layer          = 48
+llm_load_print_meta: n_head           = 48
+llm_load_print_meta: n_head_kv        = 8
+llm_load_print_meta: n_rot            = 128
+llm_load_print_meta: n_swa            = 0
+llm_load_print_meta: n_embd_head_k    = 128
+llm_load_print_meta: n_embd_head_v    = 128
+llm_load_print_meta: n_gqa            = 6
+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-05
+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             = 16384
+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  = 50000000.0
+llm_load_print_meta: freq_scale_train = 1
+llm_load_print_meta: n_ctx_orig_yarn  = 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       = 20B
+llm_load_print_meta: model ftype      = Q8_0
+llm_load_print_meta: model params     = 19.86 B
+llm_load_print_meta: model size       = 19.65 GiB (8.50 BPW) 
+llm_load_print_meta: general.name     = Internlm2_5 20b Chat
+llm_load_print_meta: BOS token        = 1 '<s>'
+llm_load_print_meta: EOS token        = 92542 '<|im_end|>'
+llm_load_print_meta: UNK token        = 0 '<unk>'
+llm_load_print_meta: PAD token        = 2 '</s>'
+llm_load_print_meta: LF token         = 13 '<0x0A>'
+llm_load_print_meta: EOT token        = 92542 '<|im_end|>'
+llm_load_print_meta: max token length = 384
+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 48 repeating layers to GPU
+llm_load_tensors: offloading non-repeating layers to GPU
+llm_load_tensors: offloaded 49/49 layers to GPU
+llm_load_tensors:        CPU buffer size =   576.14 MiB
+llm_load_tensors:      CUDA0 buffer size = 19550.41 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  = 50000000.0
+llama_new_context_with_model: freq_scale = 1
+llama_kv_cache_init:      CUDA0 KV buffer size =    96.00 MiB
+llama_new_context_with_model: KV self size  =   96.00 MiB, K (f16):   48.00 MiB, V (f16):   48.00 MiB
+llama_new_context_with_model:  CUDA_Host  output buffer size =     0.35 MiB
+llama_new_context_with_model:      CUDA0 compute buffer size =   192.75 MiB
+llama_new_context_with_model:  CUDA_Host compute buffer size =    13.01 MiB
+llama_new_context_with_model: graph nodes  = 1542
+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 104.292 ms
+compute_imatrix: computing over 136 chunks with batch_size 512
+compute_imatrix: 1.24 seconds per pass - ETA 2.80 minutes
+[1]4.9433,[2]3.5465,[3]3.3652,[4]3.8151,[5]3.7627,[6]3.4044,[7]3.9572,[8]3.9796,[9]4.3954,
+save_imatrix: stored collected data after 10 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[10]4.5468,[11]4.1983,[12]4.5041,[13]5.0584,[14]5.3707,[15]5.7616,[16]5.9628,[17]5.7050,[18]5.8930,[19]6.1854,
+save_imatrix: stored collected data after 20 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[20]5.9909,[21]6.0645,[22]6.1454,[23]6.1814,[24]6.1374,[25]6.2946,[26]6.4994,[27]6.6498,[28]6.6710,[29]6.7504,
+save_imatrix: stored collected data after 30 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[30]6.9566,[31]7.0051,[32]6.7743,[33]6.5221,[34]6.2758,[35]6.0577,[36]5.9228,[37]5.8361,[38]5.7738,[39]5.7151,
+save_imatrix: stored collected data after 40 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[40]5.6441,[41]5.6000,[42]5.5048,[43]5.4601,[44]5.5103,[45]5.5369,[46]5.6457,[47]5.6336,[48]5.7881,[49]5.8867,
+save_imatrix: stored collected data after 50 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[50]6.0185,[51]6.1210,[52]6.2534,[53]6.1524,[54]6.2275,[55]6.2990,[56]6.4089,[57]6.2990,[58]6.3198,[59]6.3431,
+save_imatrix: stored collected data after 60 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[60]6.4292,[61]6.5505,[62]6.6334,[63]6.6961,[64]6.7096,[65]6.7299,[66]6.7194,[67]6.6990,[68]6.6319,[69]6.6265,
+save_imatrix: stored collected data after 70 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[70]6.6806,[71]6.7003,[72]6.6329,[73]6.5933,[74]6.5994,[75]6.5656,[76]6.5599,[77]6.5486,[78]6.5601,[79]6.5212,
+save_imatrix: stored collected data after 80 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[80]6.5208,[81]6.4772,[82]6.4516,[83]6.4219,[84]6.4114,[85]6.3637,[86]6.3351,[87]6.3076,[88]6.3319,[89]6.3471,
+save_imatrix: stored collected data after 90 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[90]6.3203,[91]6.3411,[92]6.3604,[93]6.3138,[94]6.3055,[95]6.2836,[96]6.3029,[97]6.3051,[98]6.3081,[99]6.2754,
+save_imatrix: stored collected data after 100 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[100]6.2523,[101]6.2147,[102]6.1763,[103]6.1504,[104]6.1218,[105]6.0938,[106]6.0704,[107]6.0809,[108]6.1147,[109]6.1776,
+save_imatrix: stored collected data after 110 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[110]6.2404,[111]6.2917,[112]6.3841,[113]6.4398,[114]6.4649,[115]6.4649,[116]6.4770,[117]6.4682,[118]6.4559,[119]6.4013,
+save_imatrix: stored collected data after 120 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[120]6.3480,[121]6.3764,[122]6.3763,[123]6.3789,[124]6.3993,[125]6.4365,[126]6.4602,[127]6.4740,[128]6.4975,[129]6.5216,
+save_imatrix: stored collected data after 130 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+[130]6.4913,[131]6.5093,[132]6.5505,[133]6.5754,[134]6.6208,[135]6.6547,[136]6.6856,
+save_imatrix: stored collected data after 136 chunks in internlm2_5-20b-chat-IMat-GGUF/imatrix.dat
+
+llama_print_timings:        load time =    8844.06 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 =  159644.81 ms / 69632 tokens (    2.29 ms per token,   436.17 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 =  167992.10 ms / 69633 tokens
+
+Final estimate: PPL = 6.6856 +/- 0.08686