Eval bug: phi 4 - input is empty #11157
Description
Name and Version
build: 4451 (d9feae1) with MSVC 19.29.30157.0 for
Operating systems
Windows
GGML backends
CUDA
Hardware
Ryzen 7950x3d + RTX 3090
Models
phi 4
Problem description & steps to reproduce
phi 4 - input is empty
Just load model
First Bad Commit
No response
Relevant log output
llama-cli.exe --model models/new3/phi-4-Q8_0.gguf --color --threads 30 --keep -1 --n-predict -1 --ctx-size 16384 --interactive -ngl 99 --simple-io -e --multiline-input --no-display-prompt --conversation --no-mmap
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
build: 4451 (d9feae1c) with MSVC 19.29.30157.0 for
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file: using device CUDA0 (NVIDIA GeForce RTX 3090) - 23306 MiB free
llama_model_loader: loaded meta data with 37 key-value pairs and 243 tensors from models/new3/phi-4-Q8_0.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 = phi3
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Phi 4
llama_model_loader: - kv 3: general.version str = 4
llama_model_loader: - kv 4: general.organization str = Microsoft
llama_model_loader: - kv 5: general.basename str = phi
llama_model_loader: - kv 6: general.size_label str = 15B
llama_model_loader: - kv 7: general.license str = mit
llama_model_loader: - kv 8: general.license.link str = https://huggingface.co/microsoft/phi-...
llama_model_loader: - kv 9: general.tags arr[str,7] = ["phi", "nlp", "math", "code", "chat"...
llama_model_loader: - kv 10: general.languages arr[str,1] = ["en"]
llama_model_loader: - kv 11: phi3.context_length u32 = 16384
llama_model_loader: - kv 12: phi3.rope.scaling.original_context_length u32 = 16384
llama_model_loader: - kv 13: phi3.embedding_length u32 = 5120
llama_model_loader: - kv 14: phi3.feed_forward_length u32 = 17920
llama_model_loader: - kv 15: phi3.block_count u32 = 40
llama_model_loader: - kv 16: phi3.attention.head_count u32 = 40
llama_model_loader: - kv 17: phi3.attention.head_count_kv u32 = 10
llama_model_loader: - kv 18: phi3.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 19: phi3.rope.dimension_count u32 = 128
llama_model_loader: - kv 20: phi3.rope.freq_base f32 = 250000.000000
llama_model_loader: - kv 21: general.file_type u32 = 7
llama_model_loader: - kv 22: phi3.attention.sliding_window u32 = 0
llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 24: tokenizer.ggml.pre str = dbrx
llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,100352] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,100352] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,100000] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 28: tokenizer.ggml.bos_token_id u32 = 100257
llama_model_loader: - kv 29: tokenizer.ggml.eos_token_id u32 = 100257
llama_model_loader: - kv 30: tokenizer.ggml.padding_token_id u32 = 100257
llama_model_loader: - kv 31: tokenizer.chat_template str = {% for message in messages %}{% if (m...
llama_model_loader: - kv 32: general.quantization_version u32 = 2
llama_model_loader: - kv 33: quantize.imatrix.file str = /models_out/phi-4-GGUF/phi-4.imatrix
llama_model_loader: - kv 34: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: - kv 35: quantize.imatrix.entries_count i32 = 160
llama_model_loader: - kv 36: quantize.imatrix.chunks_count i32 = 127
llama_model_loader: - type f32: 81 tensors
llama_model_loader: - type q8_0: 162 tensors
llm_load_vocab: special tokens cache size = 96
llm_load_vocab: token to piece cache size = 0.6151 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = phi3
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 100352
llm_load_print_meta: n_merges = 100000
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 16384
llm_load_print_meta: n_embd = 5120
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_head = 40
llm_load_print_meta: n_head_kv = 10
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1280
llm_load_print_meta: n_embd_v_gqa = 1280
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 17920
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 250000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 16384
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 14B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 14.66 B
llm_load_print_meta: model size = 14.51 GiB (8.50 BPW)
llm_load_print_meta: general.name = Phi 4
llm_load_print_meta: BOS token = 100257 '<|endoftext|>'
llm_load_print_meta: EOS token = 100257 '<|endoftext|>'
llm_load_print_meta: EOT token = 100265 '<|im_end|>'
llm_load_print_meta: PAD token = 100257 '<|endoftext|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: FIM PRE token = 100258 '<|fim_prefix|>'
llm_load_print_meta: FIM SUF token = 100260 '<|fim_suffix|>'
llm_load_print_meta: FIM MID token = 100259 '<|fim_middle|>'
llm_load_print_meta: EOG token = 100257 '<|endoftext|>'
llm_load_print_meta: EOG token = 100265 '<|im_end|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: offloading 40 repeating layers to GPU
llm_load_tensors: offloading output layer to GPU
llm_load_tensors: offloaded 41/41 layers to GPU
llm_load_tensors: CUDA_Host model buffer size = 520.62 MiB
llm_load_tensors: CUDA0 model buffer size = 14334.71 MiB
.....................................................................................
llama_new_context_with_model: n_seq_max = 1
llama_new_context_with_model: n_ctx = 16384
llama_new_context_with_model: n_ctx_per_seq = 16384
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 250000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: kv_size = 16384, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 40, can_shift = 1
llama_kv_cache_init: CUDA0 KV buffer size = 3200.00 MiB
llama_new_context_with_model: KV self size = 3200.00 MiB, K (f16): 1600.00 MiB, V (f16): 1600.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.38 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 1357.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 42.01 MiB
llama_new_context_with_model: graph nodes = 1606
llama_new_context_with_model: graph splits = 2
common_init_from_params: setting dry_penalty_last_n to ctx_size = 16384
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
main: llama threadpool init, n_threads = 30
main: chat template example:
<|im_start|>system<|im_sep|>You are a helpful assistant<|im_end|><|im_start|>user<|im_sep|>Hello<|im_end|><|im_start|>assistant<|im_sep|>Hi there<|im_end|><|im_start|>user<|im_sep|>How are you?<|im_end|><|im_start|>assistant<|im_sep|>
system_info: n_threads = 30 (n_threads_batch = 30) / 32 | CUDA : ARCHS = 520,610,700,750 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
input is empty