151 lines
9.6 KiB
Plaintext
151 lines
9.6 KiB
Plaintext
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 5060 Ti, compute capability 12.0, VMM: yes
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build: 6176 (5edf1592) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
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main: llama backend init
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main: load the model and apply lora adapter, if any
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llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 5060 Ti) - 15713 MiB free
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llama_model_loader: loaded meta data with 33 key-value pairs and 292 tensors from /home/frederico/llama.cpp/models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.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.type str = model
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llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 8B Instruct
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llama_model_loader: - kv 3: general.finetune str = Instruct
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llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1
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llama_model_loader: - kv 5: general.size_label str = 8B
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llama_model_loader: - kv 6: general.license str = llama3.1
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llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
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llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
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llama_model_loader: - kv 9: llama.block_count u32 = 32
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llama_model_loader: - kv 10: llama.context_length u32 = 131072
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llama_model_loader: - kv 11: llama.embedding_length u32 = 4096
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llama_model_loader: - kv 12: llama.feed_forward_length u32 = 14336
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llama_model_loader: - kv 13: llama.attention.head_count u32 = 32
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llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
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llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000
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llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
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llama_model_loader: - kv 17: general.file_type u32 = 15
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llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
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llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128
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llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
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llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
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llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
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llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
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llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
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llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
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llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
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llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
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llama_model_loader: - kv 28: general.quantization_version u32 = 2
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llama_model_loader: - kv 29: quantize.imatrix.file str = /models_out/Meta-Llama-3.1-8B-Instruc...
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llama_model_loader: - kv 30: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
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llama_model_loader: - kv 31: quantize.imatrix.entries_count i32 = 224
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llama_model_loader: - kv 32: quantize.imatrix.chunks_count i32 = 125
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llama_model_loader: - type f32: 66 tensors
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llama_model_loader: - type q4_K: 193 tensors
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llama_model_loader: - type q6_K: 33 tensors
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print_info: file format = GGUF V3 (latest)
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print_info: file type = Q4_K - Medium
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print_info: file size = 4.58 GiB (4.89 BPW)
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load: printing all EOG tokens:
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load: - 128001 ('<|end_of_text|>')
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load: - 128008 ('<|eom_id|>')
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load: - 128009 ('<|eot_id|>')
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load: special tokens cache size = 256
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load: token to piece cache size = 0.7999 MB
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print_info: arch = llama
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print_info: vocab_only = 0
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print_info: n_ctx_train = 131072
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print_info: n_embd = 4096
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print_info: n_layer = 32
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print_info: n_head = 32
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print_info: n_head_kv = 8
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print_info: n_rot = 128
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print_info: n_swa = 0
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print_info: is_swa_any = 0
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print_info: n_embd_head_k = 128
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print_info: n_embd_head_v = 128
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print_info: n_gqa = 4
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print_info: n_embd_k_gqa = 1024
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print_info: n_embd_v_gqa = 1024
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print_info: f_norm_eps = 0.0e+00
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print_info: f_norm_rms_eps = 1.0e-05
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print_info: f_clamp_kqv = 0.0e+00
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print_info: f_max_alibi_bias = 0.0e+00
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print_info: f_logit_scale = 0.0e+00
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print_info: f_attn_scale = 0.0e+00
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print_info: n_ff = 14336
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print_info: n_expert = 0
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print_info: n_expert_used = 0
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print_info: causal attn = 1
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print_info: pooling type = 0
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print_info: rope type = 0
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print_info: rope scaling = linear
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print_info: freq_base_train = 500000.0
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print_info: freq_scale_train = 1
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print_info: n_ctx_orig_yarn = 131072
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print_info: rope_finetuned = unknown
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print_info: model type = 8B
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print_info: model params = 8.03 B
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print_info: general.name = Meta Llama 3.1 8B Instruct
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print_info: vocab type = BPE
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print_info: n_vocab = 128256
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print_info: n_merges = 280147
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print_info: BOS token = 128000 '<|begin_of_text|>'
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print_info: EOS token = 128009 '<|eot_id|>'
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print_info: EOT token = 128009 '<|eot_id|>'
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print_info: EOM token = 128008 '<|eom_id|>'
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print_info: LF token = 198 'Ċ'
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print_info: EOG token = 128001 '<|end_of_text|>'
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print_info: EOG token = 128008 '<|eom_id|>'
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print_info: EOG token = 128009 '<|eot_id|>'
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print_info: max token length = 256
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load_tensors: loading model tensors, this can take a while... (mmap = true)
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load_tensors: offloading 0 repeating layers to GPU
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load_tensors: offloaded 0/33 layers to GPU
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load_tensors: CPU_Mapped model buffer size = 4685.30 MiB
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........................................................................................
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llama_context: constructing llama_context
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llama_context: n_seq_max = 1
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llama_context: n_ctx = 4096
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llama_context: n_ctx_per_seq = 4096
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llama_context: n_batch = 2048
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llama_context: n_ubatch = 512
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llama_context: causal_attn = 1
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llama_context: flash_attn = 0
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llama_context: kv_unified = false
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llama_context: freq_base = 500000.0
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llama_context: freq_scale = 1
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llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
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llama_context: CPU output buffer size = 0.49 MiB
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llama_kv_cache_unified: CPU KV buffer size = 512.00 MiB
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llama_kv_cache_unified: size = 512.00 MiB ( 4096 cells, 32 layers, 1/1 seqs), K (f16): 256.00 MiB, V (f16): 256.00 MiB
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llama_context: CUDA0 compute buffer size = 669.48 MiB
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llama_context: CUDA_Host compute buffer size = 20.01 MiB
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llama_context: graph nodes = 1126
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llama_context: graph splits = 357 (with bs=512), 1 (with bs=1)
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common_init_from_params: added <|end_of_text|> logit bias = -inf
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common_init_from_params: added <|eom_id|> logit bias = -inf
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common_init_from_params: added <|eot_id|> logit bias = -inf
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common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
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common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
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main: llama threadpool init, n_threads = 8
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system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
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sampler seed: 4251653437
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sampler params:
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repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
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dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096
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top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
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mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
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sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
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generate: n_ctx = 4096, n_batch = 2048, n_predict = -1, n_keep = 1
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llama_perf_sampler_print: sampling time = 8.87 ms / 443 runs ( 0.02 ms per token, 49943.63 tokens per second)
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llama_perf_context_print: load time = 657.52 ms
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llama_perf_context_print: prompt eval time = 492.36 ms / 243 tokens ( 2.03 ms per token, 493.55 tokens per second)
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llama_perf_context_print: eval time = 15973.53 ms / 199 runs ( 80.27 ms per token, 12.46 tokens per second)
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llama_perf_context_print: total time = 16510.15 ms / 442 tokens
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llama_perf_context_print: graphs reused = 192
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