Donato Capitella
180 lines
12 KiB
Plaintext
180 lines
12 KiB
Plaintext
ggml_vulkan: Found 1 Vulkan devices:
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ggml_vulkan: 0 = Radeon 8060S Graphics (RADV GFX1151) (radv) | uma: 1 | fp16: 1 | bf16: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat
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build: 6040 (66625a59) with cc (GCC) 15.1.1 20250719 (Red Hat 15.1.1-5) for x86_64-redhat-linux
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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 Vulkan0 (Radeon 8060S Graphics (RADV GFX1151)) - 87722 MiB free
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llama_model_loader: additional 1 GGUFs metadata loaded.
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llama_model_loader: loaded meta data with 51 key-value pairs and 628 tensors from /home/kyuz0/models/llama-4-scout-17b-16e/Q4_K_XL/Llama-4-Scout-17B-16E-Instruct-UD-Q4_K_XL-00001-of-00002.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 = llama4
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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 = Llama-4-Scout-17B-16E-Instruct
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llama_model_loader: - kv 3: general.finetune str = 16E-Instruct
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llama_model_loader: - kv 4: general.basename str = Llama-4-Scout-17B-16E-Instruct
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llama_model_loader: - kv 5: general.quantized_by str = Unsloth
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llama_model_loader: - kv 6: general.size_label str = 17B
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llama_model_loader: - kv 7: general.license str = other
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llama_model_loader: - kv 8: general.license.name str = llama4
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llama_model_loader: - kv 9: general.repo_url str = https://huggingface.co/unsloth
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llama_model_loader: - kv 10: general.base_model.count u32 = 1
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llama_model_loader: - kv 11: general.base_model.0.name str = Llama 4 Scout 17B 16E Instruct
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llama_model_loader: - kv 12: general.base_model.0.organization str = Meta Llama
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llama_model_loader: - kv 13: general.base_model.0.repo_url str = https://huggingface.co/meta-llama/Lla...
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llama_model_loader: - kv 14: general.tags arr[str,5] = ["facebook", "meta", "pytorch", "llam...
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llama_model_loader: - kv 15: general.languages arr[str,12] = ["ar", "de", "en", "es", "fr", "hi", ...
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llama_model_loader: - kv 16: llama4.block_count u32 = 48
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llama_model_loader: - kv 17: llama4.context_length u32 = 10485760
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llama_model_loader: - kv 18: llama4.embedding_length u32 = 5120
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llama_model_loader: - kv 19: llama4.feed_forward_length u32 = 16384
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llama_model_loader: - kv 20: llama4.attention.head_count u32 = 40
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llama_model_loader: - kv 21: llama4.attention.head_count_kv u32 = 8
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llama_model_loader: - kv 22: llama4.rope.freq_base f32 = 500000.000000
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llama_model_loader: - kv 23: llama4.attention.layer_norm_rms_epsilon f32 = 0.000010
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llama_model_loader: - kv 24: llama4.expert_count u32 = 16
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llama_model_loader: - kv 25: llama4.expert_used_count u32 = 1
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llama_model_loader: - kv 26: llama4.attention.key_length u32 = 128
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llama_model_loader: - kv 27: llama4.attention.value_length u32 = 128
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llama_model_loader: - kv 28: llama4.vocab_size u32 = 202048
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llama_model_loader: - kv 29: llama4.rope.dimension_count u32 = 128
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llama_model_loader: - kv 30: llama4.interleave_moe_layer_step u32 = 1
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llama_model_loader: - kv 31: llama4.expert_feed_forward_length u32 = 8192
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llama_model_loader: - kv 32: tokenizer.ggml.model str = gpt2
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llama_model_loader: - kv 33: tokenizer.ggml.pre str = llama4
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llama_model_loader: - kv 34: tokenizer.ggml.tokens arr[str,202048] = ["À", "Á", "õ", "ö", "÷", "ø", ...
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llama_model_loader: - kv 35: tokenizer.ggml.token_type arr[i32,202048] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
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llama_model_loader: - kv 36: tokenizer.ggml.merges arr[str,439802] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
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llama_model_loader: - kv 37: tokenizer.ggml.bos_token_id u32 = 200000
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llama_model_loader: - kv 38: tokenizer.ggml.eos_token_id u32 = 200008
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llama_model_loader: - kv 39: tokenizer.ggml.padding_token_id u32 = 200018
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llama_model_loader: - kv 40: tokenizer.ggml.add_bos_token bool = true
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llama_model_loader: - kv 41: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
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llama_model_loader: - kv 42: general.quantization_version u32 = 2
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llama_model_loader: - kv 43: general.file_type u32 = 15
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llama_model_loader: - kv 44: quantize.imatrix.file str = Llama-4-Scout-17B-16E-Instruct-GGUF/i...
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llama_model_loader: - kv 45: quantize.imatrix.dataset str = unsloth_calibration_Llama-4-Scout-17B...
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llama_model_loader: - kv 46: quantize.imatrix.entries_count u32 = 528
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llama_model_loader: - kv 47: quantize.imatrix.chunks_count u32 = 729
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llama_model_loader: - kv 48: split.no u16 = 0
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llama_model_loader: - kv 49: split.tensors.count i32 = 628
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llama_model_loader: - kv 50: split.count u16 = 2
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llama_model_loader: - type f32: 146 tensors
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llama_model_loader: - type q4_K: 421 tensors
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llama_model_loader: - type q5_K: 43 tensors
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llama_model_loader: - type q6_K: 18 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 = 57.73 GiB (4.60 BPW)
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load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
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load: special tokens cache size = 1135
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load: token to piece cache size = 1.3873 MB
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print_info: arch = llama4
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print_info: vocab_only = 0
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print_info: n_ctx_train = 10485760
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print_info: n_embd = 5120
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print_info: n_layer = 48
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print_info: n_head = 40
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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 = 8192
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print_info: is_swa_any = 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 = 5
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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 = 16384
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print_info: n_expert = 16
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print_info: n_expert_used = 1
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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 = 10485760
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print_info: rope_finetuned = unknown
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print_info: model type = 17Bx16E (Scout)
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print_info: model params = 107.77 B
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print_info: general.name = Llama-4-Scout-17B-16E-Instruct
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print_info: vocab type = BPE
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print_info: n_vocab = 202048
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print_info: n_merges = 439802
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print_info: BOS token = 200000 '<|begin_of_text|>'
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print_info: EOS token = 200008 '<|eot|>'
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print_info: PAD token = 200018 '<|finetune_right_pad|>'
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print_info: LF token = 198 'Ċ'
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print_info: FIM PRE token = 200002 '<|fim_prefix|>'
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print_info: FIM SUF token = 200004 '<|fim_suffix|>'
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print_info: FIM MID token = 200003 '<|fim_middle|>'
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print_info: EOG token = 200001 '<|end_of_text|>'
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print_info: EOG token = 200008 '<|eot|>'
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print_info: max token length = 192
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load_tensors: loading model tensors, this can take a while... (mmap = false)
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load_tensors: offloading 48 repeating layers to GPU
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load_tensors: offloading output layer to GPU
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load_tensors: offloaded 49/49 layers to GPU
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load_tensors: Vulkan0 model buffer size = 58558.57 MiB
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load_tensors: CPU model buffer size = 554.94 MiB
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....................................................................................................
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llama_context: constructing llama_context
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llama_context: non-unified KV cache requires ggml_set_rows() - forcing unified KV cache
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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 = 1
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llama_context: kv_unified = true
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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 (10485760) -- the full capacity of the model will not be utilized
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llama_context: Vulkan_Host output buffer size = 0.77 MiB
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llama_kv_cache_unified_iswa: creating non-SWA KV cache, size = 4096 cells
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llama_kv_cache_unified: Vulkan0 KV buffer size = 192.00 MiB
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llama_kv_cache_unified: size = 192.00 MiB ( 4096 cells, 12 layers, 1/ 1 seqs), K (f16): 96.00 MiB, V (f16): 96.00 MiB
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llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility
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llama_kv_cache_unified_iswa: creating SWA KV cache, size = 4096 cells
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llama_kv_cache_unified: Vulkan0 KV buffer size = 576.00 MiB
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llama_kv_cache_unified: size = 576.00 MiB ( 4096 cells, 36 layers, 1/ 1 seqs), K (f16): 288.00 MiB, V (f16): 288.00 MiB
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llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility
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llama_context: Vulkan0 compute buffer size = 440.63 MiB
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llama_context: Vulkan_Host compute buffer size = 26.02 MiB
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llama_context: graph nodes = 2420
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llama_context: graph splits = 2
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common_init_from_params: added <|end_of_text|> logit bias = -inf
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common_init_from_params: added <|eot|> 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 = 16
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system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
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sampler seed: 2510811977
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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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Hello (
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llama_perf_sampler_print: sampling time = 0.09 ms / 3 runs ( 0.03 ms per token, 32608.70 tokens per second)
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llama_perf_context_print: load time = 16387.21 ms
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llama_perf_context_print: prompt eval time = 291.47 ms / 2 tokens ( 145.73 ms per token, 6.86 tokens per second)
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llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
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llama_perf_context_print: total time = 319.42 ms / 3 tokens
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llama_perf_context_print: graphs reused = 0
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Elapsed #3: 17.154124582s
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Run #3 status: 0
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→ Avg over 3 runs: 20.045s
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