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llama_model_loader: loaded meta data with 31 key-value pairs and 219 tensors from Yi-Coder-1.5B-Chat-IMat-GGUF/Yi-Coder-1.5B-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              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Yi Coder 1.5B Chat
llama_model_loader: - kv   3:                           general.finetune str              = Chat
llama_model_loader: - kv   4:                           general.basename str              = Yi-Coder
llama_model_loader: - kv   5:                         general.size_label str              = 1.5B
llama_model_loader: - kv   6:                            general.license str              = apache-2.0
llama_model_loader: - kv   7:                          llama.block_count u32              = 24
llama_model_loader: - kv   8:                       llama.context_length u32              = 131072
llama_model_loader: - kv   9:                     llama.embedding_length u32              = 2048
llama_model_loader: - kv  10:                  llama.feed_forward_length u32              = 5504
llama_model_loader: - kv  11:                 llama.attention.head_count u32              = 16
llama_model_loader: - kv  12:              llama.attention.head_count_kv u32              = 16
llama_model_loader: - kv  13:                       llama.rope.freq_base f32              = 10000000.000000
llama_model_loader: - kv  14:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  15:                          general.file_type u32              = 7
llama_model_loader: - kv  16:                           llama.vocab_size u32              = 64000
llama_model_loader: - kv  17:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv  18:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  19:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  20:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  21:                      tokenizer.ggml.tokens arr[str,64000]   = ["<unk>", "<|startoftext|>", "<|endof...
llama_model_loader: - kv  22:                      tokenizer.ggml.scores arr[f32,64000]   = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,64000]   = [3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, ...
llama_model_loader: - kv  24:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  25:                tokenizer.ggml.eos_token_id u32              = 7
llama_model_loader: - kv  26:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  27:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  28:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  29:                    tokenizer.chat_template str              = {% if messages[0]['role'] == 'system'...
llama_model_loader: - kv  30:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   49 tensors
llama_model_loader: - type q8_0:  170 tensors
llm_load_vocab: special tokens cache size = 13
llm_load_vocab: token to piece cache size = 0.3834 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = llama
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 64000
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 131072
llm_load_print_meta: n_embd           = 2048
llm_load_print_meta: n_layer          = 24
llm_load_print_meta: n_head           = 16
llm_load_print_meta: n_head_kv        = 16
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            = 1
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-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             = 5504
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  = 10000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn  = 131072
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: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = ?B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 1.48 B
llm_load_print_meta: model size       = 1.46 GiB (8.50 BPW) 
llm_load_print_meta: general.name     = Yi Coder 1.5B Chat
llm_load_print_meta: BOS token        = 1 '<|startoftext|>'
llm_load_print_meta: EOS token        = 7 '<|im_end|>'
llm_load_print_meta: UNK token        = 0 '<unk>'
llm_load_print_meta: PAD token        = 0 '<unk>'
llm_load_print_meta: LF token         = 315 '<0x0A>'
llm_load_print_meta: EOT token        = 2 '<|endoftext|>'
llm_load_print_meta: max token length = 48
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 =    0.20 MiB
llm_load_tensors: offloading 24 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 25/25 layers to GPU
llm_load_tensors:        CPU buffer size =   132.81 MiB
llm_load_tensors:      CUDA0 buffer size =  1363.58 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  = 10000000.0
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 =     0.24 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =   129.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =     5.01 MiB
llama_new_context_with_model: graph nodes  = 774
llama_new_context_with_model: graph splits = 2

system_info: n_threads = 25 (n_threads_batch = 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 99.591 ms
compute_imatrix: computing over 146 chunks with batch_size 512
compute_imatrix: 0.26 seconds per pass - ETA 0.63 minutes
[1]12.3775,[2]9.0588,[3]9.6282,[4]10.7527,[5]10.4030,[6]10.8646,[7]9.4170,[8]10.3837,[9]10.3639,
save_imatrix: stored collected data after 10 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[10]11.2951,[11]11.2255,[12]10.0765,[13]10.6202,[14]11.6802,[15]11.9295,[16]12.5347,[17]13.0545,[18]13.1957,[19]13.4568,
save_imatrix: stored collected data after 20 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[20]14.0183,[21]13.1707,[22]12.9273,[23]13.2283,[24]13.2769,[25]13.3386,[26]12.9147,[27]13.3612,[28]13.6365,[29]14.0820,
save_imatrix: stored collected data after 30 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[30]14.2418,[31]14.6417,[32]15.0781,[33]15.0261,[34]14.8378,[35]14.3335,[36]13.4314,[37]12.6496,[38]12.5749,[39]12.5084,
save_imatrix: stored collected data after 40 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[40]12.4063,[41]12.0278,[42]11.6789,[43]11.4431,[44]11.1058,[45]10.8712,[46]10.8022,[47]10.9571,[48]11.1340,[49]11.3635,
save_imatrix: stored collected data after 50 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[50]11.6500,[51]12.1894,[52]12.6603,[53]12.9860,[54]13.2400,[55]13.3273,[56]13.2304,[57]13.4429,[58]13.5685,[59]13.7168,
save_imatrix: stored collected data after 60 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[60]13.5534,[61]13.3540,[62]13.3924,[63]13.5977,[64]13.7819,[65]13.9642,[66]14.1207,[67]14.2169,[68]14.2894,[69]14.3509,
save_imatrix: stored collected data after 70 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[70]14.0953,[71]13.9685,[72]13.8202,[73]13.7003,[74]13.7496,[75]13.8581,[76]13.8593,[77]13.8964,[78]13.8617,[79]13.8240,
save_imatrix: stored collected data after 80 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[80]13.7610,[81]13.6147,[82]13.6576,[83]13.6011,[84]13.5473,[85]13.5541,[86]13.4621,[87]13.3679,[88]13.2938,[89]13.2863,
save_imatrix: stored collected data after 90 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[90]13.2467,[91]13.2158,[92]13.0646,[93]13.0258,[94]13.1250,[95]13.1409,[96]13.0703,[97]13.1099,[98]13.1388,[99]13.2176,
save_imatrix: stored collected data after 100 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[100]13.0150,[101]13.0928,[102]13.1252,[103]13.1739,[104]13.1938,[105]13.2283,[106]13.0860,[107]12.9600,[108]12.8287,[109]12.6830,
save_imatrix: stored collected data after 110 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[110]12.5595,[111]12.4451,[112]12.3245,[113]12.2095,[114]12.1711,[115]12.2043,[116]12.2625,[117]12.4031,[118]12.5382,[119]12.6627,
save_imatrix: stored collected data after 120 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[120]12.8603,[121]13.0042,[122]13.0423,[123]13.0678,[124]12.9864,[125]12.9969,[126]12.9487,[127]12.8795,[128]12.7838,[129]12.7956,
save_imatrix: stored collected data after 130 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[130]12.8712,[131]12.8948,[132]12.9537,[133]13.0036,[134]13.0661,[135]13.1116,[136]13.1231,[137]13.1481,[138]13.1283,[139]13.1007,
save_imatrix: stored collected data after 140 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat
[140]13.2059,[141]13.3078,[142]13.4163,[143]13.5452,[144]13.6686,[145]13.7820,[146]13.8676,
save_imatrix: stored collected data after 146 chunks in Yi-Coder-1.5B-Chat-IMat-GGUF/imatrix.dat

llama_print_timings:        load time =     754.58 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 =   26818.89 ms / 74752 tokens (    0.36 ms per token,  2787.29 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 =   27860.91 ms / 74753 tokens

Final estimate: PPL = 13.8676 +/- 0.22522