Eval bug: unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K_XL using HIP backend (AMD MI300X) outputs `GGGGG`
Issue Details
yeahdongcnAuthor
Name and Version
root@rocm-jupyter-gpu-mi300x1-192gb-devcloud-atl1:~/llama.cpp# ./build/bin/llama-cli --version 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: AMD Instinct MI300X VF, gfx942:sramecc+:xnack- (0x942), VMM: no, Wave Size: 64 version: 5964 (acd6cb1c) built with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
Operating systems
Linux
GGML backends
HIP
Hardware
AMD MI300X
Models
unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K_XL
Problem description & steps to reproduce
./build/bin/llama-cli -hf unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K_XL -ngl 999
First Bad Commit
No response
Relevant log output
root@rocm-jupyter-gpu-mi300x1-192gb-devcloud-atl1:~/llama.cpp# ./build/bin/llama-cli -hf unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K_XL -ngl 999 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: AMD Instinct MI300X VF, gfx942:sramecc+:xnack- (0x942), VMM: no, Wave Size: 64 curl_perform_with_retry: HEAD https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/resolve/main/UD-Q2_K_XL/Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00001-of-00004.gguf (attempt 1 of 1)... common_download_file_single: using cached file: /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00001-of-00004.gguf curl_perform_with_retry: HEAD https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/resolve/main/UD-Q2_K_XL/Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00003-of-00004.gguf (attempt 1 of 1)... curl_perform_with_retry: HEAD https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/resolve/main/UD-Q2_K_XL/Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00004-of-00004.gguf (attempt 1 of 1)... curl_perform_with_retry: HEAD https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/resolve/main/UD-Q2_K_XL/Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00002-of-00004.gguf (attempt 1 of 1)... common_download_file_single: using cached file: /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00002-of-00004.gguf common_download_file_single: using cached file: /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00003-of-00004.gguf common_download_file_single: using cached file: /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00004-of-00004.gguf build: 5964 (acd6cb1c) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu main: llama backend init main: load the model and apply lora adapter, if any llama_model_load_from_file_impl: using device ROCm0 (AMD Instinct MI300X VF) - 195958 MiB free llama_model_loader: additional 3 GGUFs metadata loaded. llama_model_loader: loaded meta data with 47 key-value pairs and 747 tensors from /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00001-of-00004.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 = qwen3moe llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen3-Coder-480B-A35B-Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Qwen3-Coder-480B-A35B-Instruct llama_model_loader: - kv 5: general.quantized_by str = Unsloth llama_model_loader: - kv 6: general.size_label str = 480B-A35B llama_model_loader: - kv 7: general.license str = apache-2.0 llama_model_loader: - kv 8: general.license.link str = https://huggingface.co/Qwen/Qwen3-Cod... 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 = Qwen3 Coder 480B A35B Instruct llama_model_loader: - kv 12: general.base_model.0.organization str = Qwen llama_model_loader: - kv 13: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3-Cod... llama_model_loader: - kv 14: general.tags arr[str,2] = ["unsloth", "text-generation"] llama_model_loader: - kv 15: qwen3moe.block_count u32 = 62 llama_model_loader: - kv 16: qwen3moe.context_length u32 = 262144 llama_model_loader: - kv 17: qwen3moe.embedding_length u32 = 6144 llama_model_loader: - kv 18: qwen3moe.feed_forward_length u32 = 8192 llama_model_loader: - kv 19: qwen3moe.attention.head_count u32 = 96 llama_model_loader: - kv 20: qwen3moe.attention.head_count_kv u32 = 8 llama_model_loader: - kv 21: qwen3moe.rope.freq_base f32 = 10000000.000000 llama_model_loader: - kv 22: qwen3moe.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 23: qwen3moe.expert_used_count u32 = 8 llama_model_loader: - kv 24: qwen3moe.attention.key_length u32 = 128 llama_model_loader: - kv 25: qwen3moe.attention.value_length u32 = 128 llama_model_loader: - kv 26: qwen3moe.expert_count u32 = 160 llama_model_loader: - kv 27: qwen3moe.expert_feed_forward_length u32 = 2560 llama_model_loader: - kv 28: qwen3moe.expert_shared_feed_forward_length u32 = 0 llama_model_loader: - kv 29: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 30: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 31: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 32: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 33: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 34: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 35: tokenizer.ggml.padding_token_id u32 = 151654 llama_model_loader: - kv 36: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 37: tokenizer.chat_template str = {% macro render_item_list(item_list, ... llama_model_loader: - kv 38: general.quantization_version u32 = 2 llama_model_loader: - kv 39: general.file_type u32 = 10 llama_model_loader: - kv 40: quantize.imatrix.file str = Qwen3-Coder-480B-A35B-Instruct-GGUF/i... llama_model_loader: - kv 41: quantize.imatrix.dataset str = unsloth_calibration_Qwen3-Coder-480B-... llama_model_loader: - kv 42: quantize.imatrix.entries_count u32 = 434 llama_model_loader: - kv 43: quantize.imatrix.chunks_count u32 = 694 llama_model_loader: - kv 44: split.no u16 = 0 llama_model_loader: - kv 45: split.tensors.count i32 = 747 llama_model_loader: - kv 46: split.count u16 = 4 llama_model_loader: - type f32: 311 tensors llama_model_loader: - type q2_K: 124 tensors llama_model_loader: - type q3_K: 52 tensors llama_model_loader: - type q4_K: 234 tensors llama_model_loader: - type q5_K: 15 tensors llama_model_loader: - type q6_K: 11 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q2_K - Medium print_info: file size = 167.91 GiB (3.00 BPW) load: special tokens cache size = 26 load: token to piece cache size = 0.9311 MB print_info: arch = qwen3moe print_info: vocab_only = 0 print_info: n_ctx_train = 262144 print_info: n_embd = 6144 print_info: n_layer = 62 print_info: n_head = 96 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 = 12 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-06 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 = 8192 print_info: n_expert = 160 print_info: n_expert_used = 8 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 2 print_info: rope scaling = linear print_info: freq_base_train = 10000000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 262144 print_info: rope_finetuned = unknown print_info: model type = ?B print_info: model params = 480.15 B print_info: general.name = Qwen3-Coder-480B-A35B-Instruct print_info: n_ff_exp = 2560 print_info: vocab type = BPE print_info: n_vocab = 151936 print_info: n_merges = 151387 print_info: BOS token = 11 ',' print_info: EOS token = 151645 '<|im_end|>' print_info: EOT token = 151645 '<|im_end|>' print_info: PAD token = 151654 '<|vision_pad|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 151659 '<|fim_prefix|>' print_info: FIM SUF token = 151661 '<|fim_suffix|>' print_info: FIM MID token = 151660 '<|fim_middle|>' print_info: FIM PAD token = 151662 '<|fim_pad|>' print_info: FIM REP token = 151663 '<|repo_name|>' print_info: FIM SEP token = 151664 '<|file_sep|>' print_info: EOG token = 151643 '<|endoftext|>' print_info: EOG token = 151645 '<|im_end|>' print_info: EOG token = 151662 '<|fim_pad|>' print_info: EOG token = 151663 '<|repo_name|>' print_info: EOG token = 151664 '<|file_sep|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = true) load_tensors: offloading 62 repeating layers to GPU load_tensors: offloading output layer to GPU load_tensors: offloaded 63/63 layers to GPU load_tensors: ROCm0 model buffer size = 171435.90 MiB load_tensors: CPU_Mapped model buffer size = 500.77 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 = 0 llama_context: kv_unified = true llama_context: freq_base = 10000000.0 llama_context: freq_scale = 1 llama_context: n_ctx_per_seq (4096) < n_ctx_train (262144) -- the full capacity of the model will not be utilized llama_context: ROCm_Host output buffer size = 0.58 MiB llama_kv_cache_unified: ROCm0 KV buffer size = 992.00 MiB llama_kv_cache_unified: size = 992.00 MiB ( 4096 cells, 62 layers, 1/ 1 seqs), K (f16): 496.00 MiB, V (f16): 496.00 MiB llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility llama_context: ROCm0 compute buffer size = 848.00 MiB llama_context: ROCm_Host compute buffer size = 20.01 MiB llama_context: graph nodes = 4222 llama_context: graph splits = 2 common_init_from_params: added <|endoftext|> logit bias = -inf common_init_from_params: added <|im_end|> logit bias = -inf common_init_from_params: added <|fim_pad|> logit bias = -inf common_init_from_params: added <|repo_name|> logit bias = -inf common_init_from_params: added <|file_sep|> 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 = 20 main: chat template is available, enabling conversation mode (disable it with -no-cnv) main: chat template example: <|im_start|>system You are a helpful assistant<|im_end|> <|im_start|>user Hello<|im_end|> <|im_start|>assistant Hi there<|im_end|> <|im_start|>user How are you?<|im_end|> <|im_start|>assistant system_info: n_threads = 20 (n_threads_batch = 20) / 20 | ROCm : NO_VMM = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 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 | main: interactive mode on. sampler seed: 1072039959 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 = 0 == Running in interactive mode. == - Press Ctrl+C to interject at any time. - Press Return to return control to the AI. - To return control without starting a new line, end your input with '/'. - If you want to submit another line, end your input with '\'. - Not using system message. To change it, set a different value via -sys PROMPT > Hi GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG >
Issue Details
Eval bug: unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K_XL using HIP backend (AMD MI300X) outputs `GGGGG`
yeahdongcnAuthor
Name and Version
root@rocm-jupyter-gpu-mi300x1-192gb-devcloud-atl1:~/llama.cpp# ./build/bin/llama-cli --version 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: AMD Instinct MI300X VF, gfx942:sramecc+:xnack- (0x942), VMM: no, Wave Size: 64 version: 5964 (acd6cb1c) built with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
Operating systems
Linux
GGML backends
HIP
Hardware
AMD MI300X
Models
unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K_XL
Problem description & steps to reproduce
./build/bin/llama-cli -hf unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K_XL -ngl 999
First Bad Commit
No response
Relevant log output
root@rocm-jupyter-gpu-mi300x1-192gb-devcloud-atl1:~/llama.cpp# ./build/bin/llama-cli -hf unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K_XL -ngl 999 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: AMD Instinct MI300X VF, gfx942:sramecc+:xnack- (0x942), VMM: no, Wave Size: 64 curl_perform_with_retry: HEAD https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/resolve/main/UD-Q2_K_XL/Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00001-of-00004.gguf (attempt 1 of 1)... common_download_file_single: using cached file: /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00001-of-00004.gguf curl_perform_with_retry: HEAD https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/resolve/main/UD-Q2_K_XL/Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00003-of-00004.gguf (attempt 1 of 1)... curl_perform_with_retry: HEAD https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/resolve/main/UD-Q2_K_XL/Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00004-of-00004.gguf (attempt 1 of 1)... curl_perform_with_retry: HEAD https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/resolve/main/UD-Q2_K_XL/Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00002-of-00004.gguf (attempt 1 of 1)... common_download_file_single: using cached file: /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00002-of-00004.gguf common_download_file_single: using cached file: /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00003-of-00004.gguf common_download_file_single: using cached file: /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00004-of-00004.gguf build: 5964 (acd6cb1c) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu main: llama backend init main: load the model and apply lora adapter, if any llama_model_load_from_file_impl: using device ROCm0 (AMD Instinct MI300X VF) - 195958 MiB free llama_model_loader: additional 3 GGUFs metadata loaded. llama_model_loader: loaded meta data with 47 key-value pairs and 747 tensors from /root/.cache/llama.cpp/unsloth_Qwen3-Coder-480B-A35B-Instruct-GGUF_UD-Q2_K_XL_Qwen3-Coder-480B-A35B-Instruct-UD-Q2_K_XL-00001-of-00004.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 = qwen3moe llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen3-Coder-480B-A35B-Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Qwen3-Coder-480B-A35B-Instruct llama_model_loader: - kv 5: general.quantized_by str = Unsloth llama_model_loader: - kv 6: general.size_label str = 480B-A35B llama_model_loader: - kv 7: general.license str = apache-2.0 llama_model_loader: - kv 8: general.license.link str = https://huggingface.co/Qwen/Qwen3-Cod... 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 = Qwen3 Coder 480B A35B Instruct llama_model_loader: - kv 12: general.base_model.0.organization str = Qwen llama_model_loader: - kv 13: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3-Cod... llama_model_loader: - kv 14: general.tags arr[str,2] = ["unsloth", "text-generation"] llama_model_loader: - kv 15: qwen3moe.block_count u32 = 62 llama_model_loader: - kv 16: qwen3moe.context_length u32 = 262144 llama_model_loader: - kv 17: qwen3moe.embedding_length u32 = 6144 llama_model_loader: - kv 18: qwen3moe.feed_forward_length u32 = 8192 llama_model_loader: - kv 19: qwen3moe.attention.head_count u32 = 96 llama_model_loader: - kv 20: qwen3moe.attention.head_count_kv u32 = 8 llama_model_loader: - kv 21: qwen3moe.rope.freq_base f32 = 10000000.000000 llama_model_loader: - kv 22: qwen3moe.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 23: qwen3moe.expert_used_count u32 = 8 llama_model_loader: - kv 24: qwen3moe.attention.key_length u32 = 128 llama_model_loader: - kv 25: qwen3moe.attention.value_length u32 = 128 llama_model_loader: - kv 26: qwen3moe.expert_count u32 = 160 llama_model_loader: - kv 27: qwen3moe.expert_feed_forward_length u32 = 2560 llama_model_loader: - kv 28: qwen3moe.expert_shared_feed_forward_length u32 = 0 llama_model_loader: - kv 29: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 30: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 31: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 32: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 33: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 34: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 35: tokenizer.ggml.padding_token_id u32 = 151654 llama_model_loader: - kv 36: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 37: tokenizer.chat_template str = {% macro render_item_list(item_list, ... llama_model_loader: - kv 38: general.quantization_version u32 = 2 llama_model_loader: - kv 39: general.file_type u32 = 10 llama_model_loader: - kv 40: quantize.imatrix.file str = Qwen3-Coder-480B-A35B-Instruct-GGUF/i... llama_model_loader: - kv 41: quantize.imatrix.dataset str = unsloth_calibration_Qwen3-Coder-480B-... llama_model_loader: - kv 42: quantize.imatrix.entries_count u32 = 434 llama_model_loader: - kv 43: quantize.imatrix.chunks_count u32 = 694 llama_model_loader: - kv 44: split.no u16 = 0 llama_model_loader: - kv 45: split.tensors.count i32 = 747 llama_model_loader: - kv 46: split.count u16 = 4 llama_model_loader: - type f32: 311 tensors llama_model_loader: - type q2_K: 124 tensors llama_model_loader: - type q3_K: 52 tensors llama_model_loader: - type q4_K: 234 tensors llama_model_loader: - type q5_K: 15 tensors llama_model_loader: - type q6_K: 11 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q2_K - Medium print_info: file size = 167.91 GiB (3.00 BPW) load: special tokens cache size = 26 load: token to piece cache size = 0.9311 MB print_info: arch = qwen3moe print_info: vocab_only = 0 print_info: n_ctx_train = 262144 print_info: n_embd = 6144 print_info: n_layer = 62 print_info: n_head = 96 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 = 12 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-06 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 = 8192 print_info: n_expert = 160 print_info: n_expert_used = 8 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 2 print_info: rope scaling = linear print_info: freq_base_train = 10000000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 262144 print_info: rope_finetuned = unknown print_info: model type = ?B print_info: model params = 480.15 B print_info: general.name = Qwen3-Coder-480B-A35B-Instruct print_info: n_ff_exp = 2560 print_info: vocab type = BPE print_info: n_vocab = 151936 print_info: n_merges = 151387 print_info: BOS token = 11 ',' print_info: EOS token = 151645 '<|im_end|>' print_info: EOT token = 151645 '<|im_end|>' print_info: PAD token = 151654 '<|vision_pad|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 151659 '<|fim_prefix|>' print_info: FIM SUF token = 151661 '<|fim_suffix|>' print_info: FIM MID token = 151660 '<|fim_middle|>' print_info: FIM PAD token = 151662 '<|fim_pad|>' print_info: FIM REP token = 151663 '<|repo_name|>' print_info: FIM SEP token = 151664 '<|file_sep|>' print_info: EOG token = 151643 '<|endoftext|>' print_info: EOG token = 151645 '<|im_end|>' print_info: EOG token = 151662 '<|fim_pad|>' print_info: EOG token = 151663 '<|repo_name|>' print_info: EOG token = 151664 '<|file_sep|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = true) load_tensors: offloading 62 repeating layers to GPU load_tensors: offloading output layer to GPU load_tensors: offloaded 63/63 layers to GPU load_tensors: ROCm0 model buffer size = 171435.90 MiB load_tensors: CPU_Mapped model buffer size = 500.77 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 = 0 llama_context: kv_unified = true llama_context: freq_base = 10000000.0 llama_context: freq_scale = 1 llama_context: n_ctx_per_seq (4096) < n_ctx_train (262144) -- the full capacity of the model will not be utilized llama_context: ROCm_Host output buffer size = 0.58 MiB llama_kv_cache_unified: ROCm0 KV buffer size = 992.00 MiB llama_kv_cache_unified: size = 992.00 MiB ( 4096 cells, 62 layers, 1/ 1 seqs), K (f16): 496.00 MiB, V (f16): 496.00 MiB llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility llama_context: ROCm0 compute buffer size = 848.00 MiB llama_context: ROCm_Host compute buffer size = 20.01 MiB llama_context: graph nodes = 4222 llama_context: graph splits = 2 common_init_from_params: added <|endoftext|> logit bias = -inf common_init_from_params: added <|im_end|> logit bias = -inf common_init_from_params: added <|fim_pad|> logit bias = -inf common_init_from_params: added <|repo_name|> logit bias = -inf common_init_from_params: added <|file_sep|> 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 = 20 main: chat template is available, enabling conversation mode (disable it with -no-cnv) main: chat template example: <|im_start|>system You are a helpful assistant<|im_end|> <|im_start|>user Hello<|im_end|> <|im_start|>assistant Hi there<|im_end|> <|im_start|>user How are you?<|im_end|> <|im_start|>assistant system_info: n_threads = 20 (n_threads_batch = 20) / 20 | ROCm : NO_VMM = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 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 | main: interactive mode on. sampler seed: 1072039959 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 = 0 == Running in interactive mode. == - Press Ctrl+C to interject at any time. - Press Return to return control to the AI. - To return control without starting a new line, end your input with '/'. - If you want to submit another line, end your input with '\'. - Not using system message. To change it, set a different value via -sys PROMPT > Hi GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG >