Repository: EleutherAI/lm-evaluation-harness
Issue: Ubelievable long time when host the gguf mode ?
Root cause: Configuration Error

lm_eval --model gguf --tasks arc_challenge --num_fewshot 25 --model_args model=codellama,base_url=http://127.0.0.1:8090 --batch_size 16  --log_samples --outp
ut_path ./hzg_llama3_arc_challenge_25shot_f16GGUF --show_config   --cache_requests true --use_ca
che ./hzg_llama3_arc_challenge_25shot_f16GGUF --verbosity DEBUG
Checking cached requests: 100%|███████████████████████████| 4687/4687 [00:00<00:00, 5980.62it/s]
  0%|                                                     | 1/4687 [02:32<199:02:30, 152.91s/it]


####(base) ubuntu@ly-rq-214-23-49-1:~$ python -m llama_cpp.server --config_file llama_cpp_config.json
llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from /home/ubuntu/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3-8B-Instruct/Llama-3-8B-Instruct-f16.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.name str              = c4a54320a52ed5f88b7a2f84496903ea4ff07b45
llama_model_loader: - kv   2:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv   3:                       llama.context_length u32              = 8192
llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv   5:                          llama.block_count u32              = 32
llama_model_loader: - kv   6:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv   7:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   8:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv   9:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  10:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  11:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv  12:                          general.file_type u32              = 1
llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  14:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  15:                      tokenizer.ggml.scores arr[f32,128256]  = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  20:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type  f16:  226 tensors
llm_load_vocab: missing pre-tokenizer type, using: 'default'
llm_load_vocab:                                             
llm_load_vocab: ************************************        
llm_load_vocab: GENERATION QUALITY WILL BE DEGRADED!        
llm_load_vocab: CONSIDER REGENERATING THE MODEL             
llm_load_vocab: ************************************        
llm_load_vocab:                                             
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.8000 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = llama
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 128256
llm_load_print_meta: n_merges         = 280147
llm_load_print_meta: n_ctx_train      = 8192
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_rot            = 128
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     = 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-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             = 14336
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        = 0
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 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       = 8B
llm_load_print_meta: model ftype      = F16
llm_load_print_meta: model params     = 8.03 B
llm_load_print_meta: model size       = 14.96 GiB (16.00 BPW) 
llm_load_print_meta: general.name     = c4a54320a52ed5f88b7a2f84496903ea4ff07b45
llm_load_print_meta: BOS token        = 128000 '
'
llm_load_print_meta: EOS token        = 128009 '
'
llm_load_print_meta: LF token         = 128 'Ä'
llm_load_print_meta: EOT token        = 128009 '
'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 4 CUDA devices:
  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
  Device 1: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
  Device 2: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
  Device 3: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size =    0.74 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors:        CPU buffer size =  1002.00 MiB
llm_load_tensors:      CUDA0 buffer size =  3744.28 MiB
llm_load_tensors:      CUDA1 buffer size =  3328.25 MiB
llm_load_tensors:      CUDA2 buffer size =  3328.25 MiB
llm_load_tensors:      CUDA3 buffer size =  3914.23 MiB
..........................................................................................
llama_new_context_with_model: n_ctx      = 16128
llama_new_context_with_model: n_batch    = 512
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: freq_base  = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      CUDA0 KV buffer size =   567.00 MiB
llama_kv_cache_init:      CUDA1 KV buffer size =   504.00 MiB
llama_kv_cache_init:      CUDA2 KV buffer size =   504.00 MiB
llama_kv_cache_init:      CUDA3 KV buffer size =   441.00 MiB
llama_new_context_with_model: KV self size  = 2016.00 MiB, K (f16): 1008.00 MiB, V (f16): 1008.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.50 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=4)
llama_new_context_with_model:      CUDA0 compute buffer size =   245.76 MiB
llama_new_context_with_model:      CUDA1 compute buffer size =   175.01 MiB
llama_new_context_with_model:      CUDA2 compute buffer size =   175.01 MiB
llama_new_context_with_model:      CUDA3 compute buffer size =   353.52 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =   134.02 MiB
llama_new_context_with_model: graph nodes  = 903
llama_new_context_with_model: graph splits = 5
AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | 
Model metadata: {'tokenizer.chat_template': "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '
' + message['role'] + '
\n\n'+ message['content'] | trim + '
' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '
assistant
\n\n' }}{% endif %}", 'tokenizer.ggml.eos_token_id': '128009', 'tokenizer.ggml.model': 'gpt2', 'general.architecture': 'llama', 'llama.rope.freq_base': '500000.000000', 'llama.context_length': '8192', 'general.name': 'c4a54320a52ed5f88b7a2f84496903ea4ff07b45', 'llama.vocab_size': '128256', 'general.file_type': '1', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '14336', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.dimension_count': '128', 'tokenizer.ggml.bos_token_id': '128000', 'llama.attention.head_count': '32', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8'}
Available chat formats from metadata: chat_template.default
INFO:     Started server process [835603]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)


###4X RTX 4090


##llamacpp config file :

{
    "host": "0.0.0.0",
    "port": 8090,
    "models": [
        {
            "model": "/home/ubuntu/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3-8B-Instruct/Llama-3-8B-Instruct-f16.GGUF",
            "model_alias": "codellama",
            "chat_format": "llama-3",
            "n_gpu_layers": -1,
            "offload_kqv": true,
            "n_threads": 12,
            "n_batch":512,
            "flash_attn":1,
            "n_ctx": 16000
        }
    ]
}


Why do you think the problem is with lm-evaluation-harness and should be reported here?
Did you search [other GGUF related issues](https://github.com/EleutherAI/lm-evaluation-harness/issues?q=is%3Aissue+gguf) for the same problem and solutions? For example, https://github.com/EleutherAI/lm-evaluation-harness/issues/1472

THANS ,I just can not figure out the solution.