ggml_vulkan: Found 1 Vulkan devices: 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 build: 6040 (66625a59) with cc (GCC) 15.1.1 20250719 (Red Hat 15.1.1-5) for x86_64-redhat-linux main: llama backend init main: load the model and apply lora adapter, if any llama_model_load_from_file_impl: using device Vulkan0 (Radeon 8060S Graphics (RADV GFX1151)) - 87722 MiB free llama_model_loader: loaded meta data with 36 key-value pairs and 724 tensors from /home/kyuz0/models/llama-3.3-Q4_K_M/llama3.3-70.6B-Q4_K_M.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 = Llama 3.1 70B Instruct 2024 12 llama_model_loader: - kv 3: general.version str = 2024-12 llama_model_loader: - kv 4: general.finetune str = Instruct llama_model_loader: - kv 5: general.basename str = Llama-3.1 llama_model_loader: - kv 6: general.size_label str = 70B llama_model_loader: - kv 7: general.license str = llama3.1 llama_model_loader: - kv 8: general.base_model.count u32 = 1 llama_model_loader: - kv 9: general.base_model.0.name str = Llama 3.1 70B llama_model_loader: - kv 10: general.base_model.0.organization str = Meta Llama llama_model_loader: - kv 11: general.base_model.0.repo_url str = https://huggingface.co/meta-llama/Lla... llama_model_loader: - kv 12: general.tags arr[str,5] = ["facebook", "meta", "pytorch", "llam... llama_model_loader: - kv 13: general.languages arr[str,7] = ["fr", "it", "pt", "hi", "es", "th", ... llama_model_loader: - kv 14: llama.block_count u32 = 80 llama_model_loader: - kv 15: llama.context_length u32 = 131072 llama_model_loader: - kv 16: llama.embedding_length u32 = 8192 llama_model_loader: - kv 17: llama.feed_forward_length u32 = 28672 llama_model_loader: - kv 18: llama.attention.head_count u32 = 64 llama_model_loader: - kv 19: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 20: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 21: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 22: llama.attention.key_length u32 = 128 llama_model_loader: - kv 23: llama.attention.value_length u32 = 128 llama_model_loader: - kv 24: general.file_type u32 = 15 llama_model_loader: - kv 25: llama.vocab_size u32 = 128256 llama_model_loader: - kv 26: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 27: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 28: tokenizer.ggml.pre str = llama-bpe llama_model_loader: - kv 29: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 30: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 31: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 32: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 33: tokenizer.ggml.eos_token_id u32 = 128009 llama_model_loader: - kv 34: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ... llama_model_loader: - kv 35: general.quantization_version u32 = 2 llama_model_loader: - type f32: 162 tensors llama_model_loader: - type q4_K: 441 tensors llama_model_loader: - type q5_K: 40 tensors llama_model_loader: - type q6_K: 81 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 39.59 GiB (4.82 BPW) load: special tokens cache size = 256 load: token to piece cache size = 0.7999 MB print_info: arch = llama print_info: vocab_only = 0 print_info: n_ctx_train = 131072 print_info: n_embd = 8192 print_info: n_layer = 80 print_info: n_head = 64 print_info: n_head_kv = 8 print_info: n_rot = 128 print_info: n_swa = 0 print_info: is_swa_any = 0 print_info: n_embd_head_k = 128 print_info: n_embd_head_v = 128 print_info: n_gqa = 8 print_info: n_embd_k_gqa = 1024 print_info: n_embd_v_gqa = 1024 print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-05 print_info: f_clamp_kqv = 0.0e+00 print_info: f_max_alibi_bias = 0.0e+00 print_info: f_logit_scale = 0.0e+00 print_info: f_attn_scale = 0.0e+00 print_info: n_ff = 28672 print_info: n_expert = 0 print_info: n_expert_used = 0 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 0 print_info: rope scaling = linear print_info: freq_base_train = 500000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 131072 print_info: rope_finetuned = unknown print_info: model type = 70B print_info: model params = 70.55 B print_info: general.name = Llama 3.1 70B Instruct 2024 12 print_info: vocab type = BPE print_info: n_vocab = 128256 print_info: n_merges = 280147 print_info: BOS token = 128000 '<|begin_of_text|>' print_info: EOS token = 128009 '<|eot_id|>' print_info: EOT token = 128009 '<|eot_id|>' print_info: EOM token = 128008 '<|eom_id|>' print_info: LF token = 198 'Ċ' print_info: EOG token = 128001 '<|end_of_text|>' print_info: EOG token = 128008 '<|eom_id|>' print_info: EOG token = 128009 '<|eot_id|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = false) load_tensors: offloading 80 repeating layers to GPU load_tensors: offloading output layer to GPU load_tensors: offloaded 81/81 layers to GPU load_tensors: Vulkan0 model buffer size = 39979.48 MiB load_tensors: CPU model buffer size = 563.62 MiB .................................................................................................. llama_context: constructing llama_context llama_context: non-unified KV cache requires ggml_set_rows() - forcing unified KV cache llama_context: n_seq_max = 1 llama_context: n_ctx = 4096 llama_context: n_ctx_per_seq = 4096 llama_context: n_batch = 2048 llama_context: n_ubatch = 512 llama_context: causal_attn = 1 llama_context: flash_attn = 1 llama_context: kv_unified = true llama_context: freq_base = 500000.0 llama_context: freq_scale = 1 llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized llama_context: Vulkan_Host output buffer size = 0.49 MiB llama_kv_cache_unified: Vulkan0 KV buffer size = 1280.00 MiB llama_kv_cache_unified: size = 1280.00 MiB ( 4096 cells, 80 layers, 1/ 1 seqs), K (f16): 640.00 MiB, V (f16): 640.00 MiB llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility llama_context: Vulkan0 compute buffer size = 266.50 MiB llama_context: Vulkan_Host compute buffer size = 24.01 MiB llama_context: graph nodes = 2647 llama_context: graph splits = 2 common_init_from_params: added <|end_of_text|> logit bias = -inf common_init_from_params: added <|eom_id|> logit bias = -inf common_init_from_params: added <|eot_id|> logit bias = -inf common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096 common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) main: llama threadpool init, n_threads = 16 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 | sampler seed: 2613669910 sampler params: repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000 dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096 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 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist generate: n_ctx = 4096, n_batch = 2048, n_predict = 1, n_keep = 1 Hello's llama_perf_sampler_print: sampling time = 0.07 ms / 3 runs ( 0.02 ms per token, 40540.54 tokens per second) llama_perf_context_print: load time = 8119.06 ms llama_perf_context_print: prompt eval time = 204.01 ms / 2 tokens ( 102.01 ms per token, 9.80 tokens per second) llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) llama_perf_context_print: total time = 225.18 ms / 3 tokens llama_perf_context_print: graphs reused = 0 Elapsed #3: 8.699816033s Run #3 status: 0 → Avg over 3 runs: 8.816s