Bug: llama-server with --system-prompt-file stops abruptly without any error #8975
Description
What happened?
following @ggerganov 's advice here I tried to run server with the following command
docker run -v /home/pritam/llm/models:/models -v /home/pritam/lipika/:/lipika -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/gemma-2-2b-it-Q4_K_M.gguf --port 8080 --host 0.0.0.0 --system-prompt-file /lipika/system.txt --logdir /lipika/logs --log-enable --log-new
The system.txt file contains around 2.3k tokens. Shortly after starting the server, the process stops abruptly without showing any error. I mentioned logdir and enabled logs but no logs were generated.
This is the the full output before the server stops
docker run -v /home/pritam/llm/models:/models -v /home/pritam/lipika/:/lipika -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/gemma-2-2b-it-Q4_K_M.gguf --port 8080 --host 0.0.0.0 --system-prompt-file /lipika/system.txt --logdir /lipika/logs --log-enable --log-new
INFO [ main] build info | tid="140734278524576" timestamp=1723315495 build=0 commit="unknown"
INFO [ main] system info | tid="140734278524576" timestamp=1723315495 n_threads=4 n_threads_batch=-1 total_threads=4 system_info="AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | "
llama_model_loader: loaded meta data with 39 key-value pairs and 288 tensors from /models/gemma-2-2b-it-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 = gemma2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Gemma 2 2b It
llama_model_loader: - kv 3: general.finetune str = it
llama_model_loader: - kv 4: general.basename str = gemma-2
llama_model_loader: - kv 5: general.size_label str = 2B
llama_model_loader: - kv 6: general.license str = gemma
llama_model_loader: - kv 7: general.tags arr[str,2] = ["conversational", "text-generation"]
llama_model_loader: - kv 8: gemma2.context_length u32 = 8192
llama_model_loader: - kv 9: gemma2.embedding_length u32 = 2304
llama_model_loader: - kv 10: gemma2.block_count u32 = 26
llama_model_loader: - kv 11: gemma2.feed_forward_length u32 = 9216
llama_model_loader: - kv 12: gemma2.attention.head_count u32 = 8
llama_model_loader: - kv 13: gemma2.attention.head_count_kv u32 = 4
llama_model_loader: - kv 14: gemma2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 15: gemma2.attention.key_length u32 = 256
llama_model_loader: - kv 16: gemma2.attention.value_length u32 = 256
llama_model_loader: - kv 17: general.file_type u32 = 15
llama_model_loader: - kv 18: gemma2.attn_logit_softcapping f32 = 50.000000
llama_model_loader: - kv 19: gemma2.final_logit_softcapping f32 = 30.000000
llama_model_loader: - kv 20: gemma2.attention.sliding_window u32 = 4096
llama_model_loader: - kv 21: tokenizer.ggml.model str = llama
llama_model_loader: - kv 22: tokenizer.ggml.pre str = default
llama_model_loader: - kv 23: tokenizer.ggml.tokens arr[str,256000] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv 24: tokenizer.ggml.scores arr[f32,256000] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 25: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 26: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 27: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 28: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 30: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 31: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 32: tokenizer.chat_template str = {{ bos_token }}{% if messages[0]['rol...
llama_model_loader: - kv 33: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 34: general.quantization_version u32 = 2
llama_model_loader: - kv 35: quantize.imatrix.file str = /models_out/gemma-2-2b-it-GGUF/gemma-...
llama_model_loader: - kv 36: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: - kv 37: quantize.imatrix.entries_count i32 = 182
llama_model_loader: - kv 38: quantize.imatrix.chunks_count i32 = 128
llama_model_loader: - type f32: 105 tensors
llama_model_loader: - type q4_K: 156 tensors
llama_model_loader: - type q6_K: 27 tensors
llm_load_vocab: special tokens cache size = 249
llm_load_vocab: token to piece cache size = 1.6014 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = gemma2
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 256000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 2304
llm_load_print_meta: n_layer = 26
llm_load_print_meta: n_head = 8
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_rot = 256
llm_load_print_meta: n_swa = 4096
llm_load_print_meta: n_embd_head_k = 256
llm_load_print_meta: n_embd_head_v = 256
llm_load_print_meta: n_gqa = 2
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
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 = 9216
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 = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 8192
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: model type = 2B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 2.61 B
llm_load_print_meta: model size = 1.59 GiB (5.21 BPW)
llm_load_print_meta: general.name = Gemma 2 2b It
llm_load_print_meta: BOS token = 2 '<bos>'
llm_load_print_meta: EOS token = 1 '<eos>'
llm_load_print_meta: UNK token = 3 '<unk>'
llm_load_print_meta: PAD token = 0 '<pad>'
llm_load_print_meta: LF token = 227 '<0x0A>'
llm_load_print_meta: EOT token = 107 '<end_of_turn>'
llm_load_print_meta: max token length = 48
llm_load_tensors: ggml ctx size = 0.13 MiB
llm_load_tensors: CPU buffer size = 1623.67 MiB
..........................................................
llama_new_context_with_model: n_ctx = 8192
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 = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 832.00 MiB
llama_new_context_with_model: KV self size = 832.00 MiB, K (f16): 416.00 MiB, V (f16): 416.00 MiB
llama_new_context_with_model: CPU output buffer size = 1.95 MiB
llama_new_context_with_model: CPU compute buffer size = 504.50 MiB
llama_new_context_with_model: graph nodes = 1050
llama_new_context_with_model: graph splits = 1
INFO [ init] initializing slots | tid="140734278524576" timestamp=1723315496 n_slots=1
INFO [ init] new slot | tid="140734278524576" timestamp=1723315496 id_slot=0 n_ctx_slot=8192
INFO [ main] model loaded | tid="140734278524576" timestamp=1723315496
INFO [ main] chat template | tid="140734278524576" timestamp=1723315496 chat_example="<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nHow are you?<end_of_turn>\n<start_of_turn>model\n" built_in=true
INFO [ main] HTTP server listening | tid="140734278524576" timestamp=1723315496 n_threads_http="3" port="8080" hostname="0.0.0.0"
VERB [ start_loop] new task may arrive | tid="140732260343456" timestamp=1723367556
VERB [ start_loop] update_multitasks | tid="140732260343456" timestamp=1723367556
VERB [ start_loop] callback_update_slots | tid="140732260343456" timestamp=1723367556
VERB [ system_prompt_update] system prompt update | tid="140732260343456" timestamp=1723367556 system_prompt="You are..."
VERB [ kv_cache_clear] clearing KV cache | tid="140732260343456" timestamp=1723367556
If I don't mention the system-prompt file, the server runs just fine.
Name and Version
I pulled the most recent version of ghcr.io/ggerganov/llama.cpp:server and tested it in my laptop and raspberry pi 5. In both cases the server stopped after about 30 seconds when system.txt was provided.
windows laptop:
version: 0 (unknown)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
raspberry pi 5:
version: 0 (unknown)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for aarch64-linux-gnu
What operating system are you seeing the problem on?
Windows, Other? (Please let us know in description)
Relevant log output
No response