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196 行
9.5 KiB
Plaintext
196 行
9.5 KiB
Plaintext
build: 5449 (8e186ef0) with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
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llama_model_loader: loaded meta data with 25 key-value pairs and 339 tensors from /models/Model-7.6B-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 = qwen2
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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 = Model
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llama_model_loader: - kv 3: general.size_label str = 7.6B
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llama_model_loader: - kv 4: qwen2.block_count u32 = 28
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llama_model_loader: - kv 5: qwen2.context_length u32 = 32768
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llama_model_loader: - kv 6: qwen2.embedding_length u32 = 3584
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llama_model_loader: - kv 7: qwen2.feed_forward_length u32 = 18944
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llama_model_loader: - kv 8: qwen2.attention.head_count u32 = 28
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llama_model_loader: - kv 9: qwen2.attention.head_count_kv u32 = 4
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llama_model_loader: - kv 10: qwen2.rope.freq_base f32 = 1000000.000000
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llama_model_loader: - kv 11: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
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llama_model_loader: - kv 12: tokenizer.ggml.model str = gpt2
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llama_model_loader: - kv 13: tokenizer.ggml.pre str = qwen2
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llama_model_loader: - kv 14: tokenizer.ggml.tokens arr[str,151700] = ["!", "\"", "#", "$", "%", "&", "'", ...
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llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,151700] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
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llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
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llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 151644
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llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 151645
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llama_model_loader: - kv 19: tokenizer.ggml.unknown_token_id u32 = 128244
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llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 151643
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llama_model_loader: - kv 21: tokenizer.ggml.add_bos_token bool = false
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llama_model_loader: - kv 22: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>...
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llama_model_loader: - kv 23: general.quantization_version u32 = 2
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llama_model_loader: - kv 24: general.file_type u32 = 15
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llama_model_loader: - type f32: 141 tensors
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llama_model_loader: - type q4_K: 169 tensors
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llama_model_loader: - type q6_K: 29 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 = 4.35 GiB (4.91 BPW)
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load: special tokens cache size = 58
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load: token to piece cache size = 0.9313 MB
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print_info: arch = qwen2
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print_info: vocab_only = 0
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print_info: n_ctx_train = 32768
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print_info: n_embd = 3584
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print_info: n_layer = 28
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print_info: n_head = 28
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print_info: n_head_kv = 4
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print_info: n_rot = 128
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print_info: n_swa = 0
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print_info: n_swa_pattern = 1
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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 = 7
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print_info: n_embd_k_gqa = 512
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print_info: n_embd_v_gqa = 512
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print_info: f_norm_eps = 0.0e+00
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print_info: f_norm_rms_eps = 1.0e-06
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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 = 18944
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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 = -1
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print_info: rope type = 2
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print_info: rope scaling = linear
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print_info: freq_base_train = 1000000.0
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print_info: freq_scale_train = 1
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print_info: n_ctx_orig_yarn = 32768
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print_info: rope_finetuned = unknown
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print_info: ssm_d_conv = 0
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print_info: ssm_d_inner = 0
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print_info: ssm_d_state = 0
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print_info: ssm_dt_rank = 0
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print_info: ssm_dt_b_c_rms = 0
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print_info: model type = 7B
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print_info: model params = 7.61 B
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print_info: general.name = Model
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print_info: vocab type = BPE
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print_info: n_vocab = 151700
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print_info: n_merges = 151387
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print_info: BOS token = 151644 '<|im_start|>'
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print_info: EOS token = 151645 '<|im_end|>'
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print_info: EOT token = 151645 '<|im_end|>'
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print_info: UNK token = 128244 '<unk>'
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print_info: PAD token = 151643 '<|endoftext|>'
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print_info: LF token = 198 'Ċ'
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print_info: FIM PRE token = 151659 '<|fim_prefix|>'
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print_info: FIM SUF token = 151661 '<|fim_suffix|>'
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print_info: FIM MID token = 151660 '<|fim_middle|>'
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print_info: FIM PAD token = 151662 '<|fim_pad|>'
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print_info: FIM REP token = 151663 '<|repo_name|>'
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print_info: FIM SEP token = 151664 '<|file_sep|>'
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print_info: EOG token = 151643 '<|endoftext|>'
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print_info: EOG token = 151645 '<|im_end|>'
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print_info: EOG token = 151662 '<|fim_pad|>'
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print_info: EOG token = 151663 '<|repo_name|>'
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print_info: EOG token = 151664 '<|file_sep|>'
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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 0/29 layers to GPU
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load_tensors: CPU_AARCH64 model buffer size = 2976.75 MiB
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load_tensors: CPU_Mapped model buffer size = 4422.31 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: freq_base = 1000000.0
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llama_context: freq_scale = 1
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llama_context: n_ctx_per_seq (4096) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
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llama_context: CPU output buffer size = 0.58 MiB
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llama_kv_cache_unified: CPU KV buffer size = 224.00 MiB
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llama_kv_cache_unified: size = 224.00 MiB ( 4096 cells, 28 layers, 1 seqs), K (f16): 112.00 MiB, V (f16): 112.00 MiB
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llama_context: CPU compute buffer size = 303.29 MiB
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llama_context: graph nodes = 1098
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llama_context: graph splits = 1
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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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clip_ctx: CLIP using CPU backend
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mtmd_cli_context: chat template example:
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<|im_start|>system
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You are a helpful assistant<|im_end|>
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<|im_start|>user
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Hello<|im_end|>
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<|im_start|>assistant
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Hi there<|im_end|>
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<|im_start|>user
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How are you?<|im_end|>
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<|im_start|>assistant
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clip_model_loader: model name:
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clip_model_loader: description: image encoder for MiniCPM-V
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clip_model_loader: GGUF version: 3
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clip_model_loader: alignment: 32
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clip_model_loader: n_tensors: 455
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clip_model_loader: n_kv: 19
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load_hparams: projector: resampler
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load_hparams: n_embd: 1152
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load_hparams: n_head: 16
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load_hparams: n_ff: 4304
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load_hparams: n_layer: 27
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load_hparams: projection_dim: 0
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load_hparams: image_size: 448
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load_hparams: patch_size: 14
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load_hparams: has_llava_proj: 0
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load_hparams: minicpmv_version: 4
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load_hparams: proj_scale_factor: 0
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load_hparams: n_wa_pattern: 0
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load_hparams: ffn_op: gelu
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load_hparams: model size: 996.02 MiB
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load_hparams: metadata size: 0.16 MiB
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alloc_compute_meta: CPU compute buffer size = 98.30 MiB
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main: loading model: /models/Model-7.6B-Q4_K_M.gguf
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encoding image or slice...
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image/slice encoded in 21585 ms
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decoding image batch 1/1, n_tokens_batch = 64
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image decoded (batch 1/1) in 9650 ms
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encoding image or slice...
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image/slice encoded in 20988 ms
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decoding image batch 1/1, n_tokens_batch = 64
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image decoded (batch 1/1) in 9145 ms
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encoding image or slice...
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image/slice encoded in 21068 ms
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decoding image batch 1/1, n_tokens_batch = 64
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image decoded (batch 1/1) in 8669 ms
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encoding image or slice...
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image/slice encoded in 21307 ms
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decoding image batch 1/1, n_tokens_batch = 64
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image decoded (batch 1/1) in 8773 ms
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encoding image or slice...
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image/slice encoded in 22171 ms
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decoding image batch 1/1, n_tokens_batch = 64
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image decoded (batch 1/1) in 9888 ms
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encoding image or slice...
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image/slice encoded in 21464 ms
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decoding image batch 1/1, n_tokens_batch = 64
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image decoded (batch 1/1) in 9315 ms
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encoding image or slice...
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image/slice encoded in 20761 ms
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decoding image batch 1/1, n_tokens_batch = 64
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image decoded (batch 1/1) in 9502 ms
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```txt
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``````
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llama_perf_context_print: load time = 4693.78 ms
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llama_perf_context_print: prompt eval time = 236384.25 ms / 567 tokens ( 416.90 ms per token, 2.40 tokens per second)
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llama_perf_context_print: eval time = 2494.74 ms / 6 runs ( 415.79 ms per token, 2.41 tokens per second)
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llama_perf_context_print: total time = 239714.30 ms / 573 tokens |