ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 ROCm devices: Device 0: Radeon 8060S Graphics, gfx1151 (0x1151), VMM: no, Wave Size: 32 build: 6040 (66625a59) with cc (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2) 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 ROCm0 (Radeon 8060S Graphics) - 124522 MiB free llama_model_loader: additional 1 GGUFs metadata loaded. 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)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama4 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Llama-4-Scout-17B-16E-Instruct llama_model_loader: - kv 3: general.finetune str = 16E-Instruct llama_model_loader: - kv 4: general.basename str = Llama-4-Scout-17B-16E-Instruct llama_model_loader: - kv 5: general.quantized_by str = Unsloth llama_model_loader: - kv 6: general.size_label str = 17B llama_model_loader: - kv 7: general.license str = other llama_model_loader: - kv 8: general.license.name str = llama4 llama_model_loader: - kv 9: general.repo_url str = https://huggingface.co/unsloth llama_model_loader: - kv 10: general.base_model.count u32 = 1 llama_model_loader: - kv 11: general.base_model.0.name str = Llama 4 Scout 17B 16E Instruct llama_model_loader: - kv 12: general.base_model.0.organization str = Meta Llama llama_model_loader: - kv 13: general.base_model.0.repo_url str = https://huggingface.co/meta-llama/Lla... llama_model_loader: - kv 14: general.tags arr[str,5] = ["facebook", "meta", "pytorch", "llam... llama_model_loader: - kv 15: general.languages arr[str,12] = ["ar", "de", "en", "es", "fr", "hi", ... llama_model_loader: - kv 16: llama4.block_count u32 = 48 llama_model_loader: - kv 17: llama4.context_length u32 = 10485760 llama_model_loader: - kv 18: llama4.embedding_length u32 = 5120 llama_model_loader: - kv 19: llama4.feed_forward_length u32 = 16384 llama_model_loader: - kv 20: llama4.attention.head_count u32 = 40 llama_model_loader: - kv 21: llama4.attention.head_count_kv u32 = 8 llama_model_loader: - kv 22: llama4.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 23: llama4.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 24: llama4.expert_count u32 = 16 llama_model_loader: - kv 25: llama4.expert_used_count u32 = 1 llama_model_loader: - kv 26: llama4.attention.key_length u32 = 128 llama_model_loader: - kv 27: llama4.attention.value_length u32 = 128 llama_model_loader: - kv 28: llama4.vocab_size u32 = 202048 llama_model_loader: - kv 29: llama4.rope.dimension_count u32 = 128 llama_model_loader: - kv 30: llama4.interleave_moe_layer_step u32 = 1 llama_model_loader: - kv 31: llama4.expert_feed_forward_length u32 = 8192 llama_model_loader: - kv 32: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 33: tokenizer.ggml.pre str = llama4 llama_model_loader: - kv 34: tokenizer.ggml.tokens arr[str,202048] = ["À", "Á", "õ", "ö", "÷", "ø", ... llama_model_loader: - kv 35: tokenizer.ggml.token_type arr[i32,202048] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 36: tokenizer.ggml.merges arr[str,439802] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 37: tokenizer.ggml.bos_token_id u32 = 200000 llama_model_loader: - kv 38: tokenizer.ggml.eos_token_id u32 = 200008 llama_model_loader: - kv 39: tokenizer.ggml.padding_token_id u32 = 200018 llama_model_loader: - kv 40: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 41: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ... llama_model_loader: - kv 42: general.quantization_version u32 = 2 llama_model_loader: - kv 43: general.file_type u32 = 15 llama_model_loader: - kv 44: quantize.imatrix.file str = Llama-4-Scout-17B-16E-Instruct-GGUF/i... llama_model_loader: - kv 45: quantize.imatrix.dataset str = unsloth_calibration_Llama-4-Scout-17B... llama_model_loader: - kv 46: quantize.imatrix.entries_count u32 = 528 llama_model_loader: - kv 47: quantize.imatrix.chunks_count u32 = 729 llama_model_loader: - kv 48: split.no u16 = 0 llama_model_loader: - kv 49: split.tensors.count i32 = 628 llama_model_loader: - kv 50: split.count u16 = 2 llama_model_loader: - type f32: 146 tensors llama_model_loader: - type q4_K: 421 tensors llama_model_loader: - type q5_K: 43 tensors llama_model_loader: - type q6_K: 18 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 57.73 GiB (4.60 BPW) load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect load: special tokens cache size = 1135 load: token to piece cache size = 1.3873 MB print_info: arch = llama4 print_info: vocab_only = 0 print_info: n_ctx_train = 10485760 print_info: n_embd = 5120 print_info: n_layer = 48 print_info: n_head = 40 print_info: n_head_kv = 8 print_info: n_rot = 128 print_info: n_swa = 8192 print_info: is_swa_any = 1 print_info: n_embd_head_k = 128 print_info: n_embd_head_v = 128 print_info: n_gqa = 5 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 = 16384 print_info: n_expert = 16 print_info: n_expert_used = 1 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 = 10485760 print_info: rope_finetuned = unknown print_info: model type = 17Bx16E (Scout) print_info: model params = 107.77 B print_info: general.name = Llama-4-Scout-17B-16E-Instruct print_info: vocab type = BPE print_info: n_vocab = 202048 print_info: n_merges = 439802 print_info: BOS token = 200000 '<|begin_of_text|>' print_info: EOS token = 200008 '<|eot|>' print_info: PAD token = 200018 '<|finetune_right_pad|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 200002 '<|fim_prefix|>' print_info: FIM SUF token = 200004 '<|fim_suffix|>' print_info: FIM MID token = 200003 '<|fim_middle|>' print_info: EOG token = 200001 '<|end_of_text|>' print_info: EOG token = 200008 '<|eot|>' print_info: max token length = 192 load_tensors: loading model tensors, this can take a while... (mmap = false) load_tensors: offloading 48 repeating layers to GPU load_tensors: offloading output layer to GPU load_tensors: offloaded 49/49 layers to GPU load_tensors: CPU model buffer size = 554.94 MiB load_tensors: ROCm0 model buffer size = 58558.57 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 (10485760) -- the full capacity of the model will not be utilized llama_context: ROCm_Host output buffer size = 0.77 MiB llama_kv_cache_unified_iswa: creating non-SWA KV cache, size = 4096 cells llama_kv_cache_unified: ROCm0 KV buffer size = 192.00 MiB 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 llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility llama_kv_cache_unified_iswa: creating SWA KV cache, size = 4096 cells llama_kv_cache_unified: ROCm0 KV buffer size = 576.00 MiB 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 llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility llama_context: ROCm0 compute buffer size = 442.62 MiB llama_context: ROCm_Host compute buffer size = 26.01 MiB llama_context: graph nodes = 2420 llama_context: graph splits = 2 common_init_from_params: added <|end_of_text|> logit bias = -inf common_init_from_params: added <|eot|> 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 | ROCm : NO_VMM = 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 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | sampler seed: 4182963810 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 The llama_perf_sampler_print: sampling time = 0.07 ms / 3 runs ( 0.02 ms per token, 46153.85 tokens per second) llama_perf_context_print: load time = 9663.18 ms llama_perf_context_print: prompt eval time = 90.98 ms / 2 tokens ( 45.49 ms per token, 21.98 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 = 110.40 ms / 3 tokens llama_perf_context_print: graphs reused = 0 Elapsed #3: 13.853856771s Run #3 status: 0 → Avg over 3 runs: 15.776s