Here is the output I got when I started the container, I hope this helps:
llama-cpp-python |
llama-cpp-python | ==========
llama-cpp-python | == CUDA ==
llama-cpp-python | ==========
llama-cpp-python |
llama-cpp-python | CUDA Version 12.4.1
llama-cpp-python |
llama-cpp-python | Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
llama-cpp-python |
llama-cpp-python | This container image and its contents are governed by the NVIDIA Deep Learning Container License.
llama-cpp-python | By pulling and using the container, you accept the terms and conditions of this license:
llama-cpp-python | https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
llama-cpp-python |
llama-cpp-python | A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
llama-cpp-python |
llama-cpp-python | None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
tokenizer_config.json: 100% 2.86k/2.86k [00:00<00:00, 18.3MB/s]
tokenizer.model: 100% 493k/493k [00:00<00:00, 5.97MB/s]
tokenizer.json: 100% 1.80M/1.80M [00:00<00:00, 3.03MB/s]
added_tokens.json: 100% 95.0/95.0 [00:00<00:00, 938kB/s]
special_tokens_map.json: 100% 660/660 [00:00<00:00, 6.67MB/s]
llama-cpp-python | Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
llama-cpp-python | llama_model_loader: loaded meta data with 25 key-value pairs and 291 tensors from /var/model/functionary-small-v2.4.Q4_0.gguf (version GGUF V3 (latest))
llama-cpp-python | llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama-cpp-python | llama_model_loader: - kv 0: general.architecture str = llama
llama-cpp-python | llama_model_loader: - kv 1: general.name str = .
llama-cpp-python | llama_model_loader: - kv 2: llama.vocab_size u32 = 32004
llama-cpp-python | llama_model_loader: - kv 3: llama.context_length u32 = 32768
llama-cpp-python | llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
llama-cpp-python | llama_model_loader: - kv 5: llama.block_count u32 = 32
llama-cpp-python | llama_model_loader: - kv 6: llama.feed_forward_length u32 = 14336
llama-cpp-python | llama_model_loader: - kv 7: llama.rope.dimension_count u32 = 128
llama-cpp-python | llama_model_loader: - kv 8: llama.attention.head_count u32 = 32
llama-cpp-python | llama_model_loader: - kv 9: llama.attention.head_count_kv u32 = 8
llama-cpp-python | llama_model_loader: - kv 10: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama-cpp-python | llama_model_loader: - kv 11: llama.rope.freq_base f32 = 1000000.000000
llama-cpp-python | llama_model_loader: - kv 12: general.file_type u32 = 2
llama-cpp-python | llama_model_loader: - kv 13: tokenizer.ggml.model str = llama
llama-cpp-python | llama_model_loader: - kv 14: tokenizer.ggml.tokens arr[str,32004] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama-cpp-python | llama_model_loader: - kv 15: tokenizer.ggml.scores arr[f32,32004] = [0.000000, 0.000000, 0.000000, 0.0000...
llama-cpp-python | llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,32004] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama-cpp-python | llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 1
llama-cpp-python | llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 2
llama-cpp-python | llama_model_loader: - kv 19: tokenizer.ggml.unknown_token_id u32 = 0
llama-cpp-python | llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 2
llama-cpp-python | llama_model_loader: - kv 21: tokenizer.ggml.add_bos_token bool = true
llama-cpp-python | llama_model_loader: - kv 22: tokenizer.ggml.add_eos_token bool = false
llama-cpp-python | llama_model_loader: - kv 23: tokenizer.chat_template str = {% for message in messages %}\n{% if m...
llama-cpp-python | llama_model_loader: - kv 24: general.quantization_version u32 = 2
llama-cpp-python | llama_model_loader: - type f32: 65 tensors
llama-cpp-python | llama_model_loader: - type q4_0: 225 tensors
llama-cpp-python | llama_model_loader: - type q6_K: 1 tensors
llama-cpp-python | llm_load_vocab: special tokens definition check successful ( 263/32004 ).
llama-cpp-python | llm_load_print_meta: format = GGUF V3 (latest)
llama-cpp-python | llm_load_print_meta: arch = llama
llama-cpp-python | llm_load_print_meta: vocab type = SPM
llama-cpp-python | llm_load_print_meta: n_vocab = 32004
llama-cpp-python | llm_load_print_meta: n_merges = 0
llama-cpp-python | llm_load_print_meta: n_ctx_train = 32768
llama-cpp-python | llm_load_print_meta: n_embd = 4096
llama-cpp-python | llm_load_print_meta: n_head = 32
llama-cpp-python | llm_load_print_meta: n_head_kv = 8
llama-cpp-python | llm_load_print_meta: n_layer = 32
llama-cpp-python | llm_load_print_meta: n_rot = 128
llama-cpp-python | llm_load_print_meta: n_embd_head_k = 128
llama-cpp-python | llm_load_print_meta: n_embd_head_v = 128
llama-cpp-python | llm_load_print_meta: n_gqa = 4
llama-cpp-python | llm_load_print_meta: n_embd_k_gqa = 1024
llama-cpp-python | llm_load_print_meta: n_embd_v_gqa = 1024
llama-cpp-python | llm_load_print_meta: f_norm_eps = 0.0e+00
llama-cpp-python | llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llama-cpp-python | llm_load_print_meta: f_clamp_kqv = 0.0e+00
llama-cpp-python | llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llama-cpp-python | llm_load_print_meta: f_logit_scale = 0.0e+00
llama-cpp-python | llm_load_print_meta: n_ff = 14336
llama-cpp-python | llm_load_print_meta: n_expert = 0
llama-cpp-python | llm_load_print_meta: n_expert_used = 0
llama-cpp-python | llm_load_print_meta: causal attn = 1
llama-cpp-python | llm_load_print_meta: pooling type = 0
llama-cpp-python | llm_load_print_meta: rope type = 0
llama-cpp-python | llm_load_print_meta: rope scaling = linear
llama-cpp-python | llm_load_print_meta: freq_base_train = 1000000.0
llama-cpp-python | llm_load_print_meta: freq_scale_train = 1
llama-cpp-python | llm_load_print_meta: n_yarn_orig_ctx = 32768
llama-cpp-python | llm_load_print_meta: rope_finetuned = unknown
llama-cpp-python | llm_load_print_meta: ssm_d_conv = 0
llama-cpp-python | llm_load_print_meta: ssm_d_inner = 0
llama-cpp-python | llm_load_print_meta: ssm_d_state = 0
llama-cpp-python | llm_load_print_meta: ssm_dt_rank = 0
llama-cpp-python | llm_load_print_meta: model type = 8B
llama-cpp-python | llm_load_print_meta: model ftype = Q4_0
llama-cpp-python | llm_load_print_meta: model params = 7.24 B
llama-cpp-python | llm_load_print_meta: model size = 3.83 GiB (4.54 BPW)
llama-cpp-python | llm_load_print_meta: general.name = .
llama-cpp-python | llm_load_print_meta: BOS token = 1 '<s>'
llama-cpp-python | llm_load_print_meta: EOS token = 2 '</s>'
llama-cpp-python | llm_load_print_meta: UNK token = 0 '<unk>'
llama-cpp-python | llm_load_print_meta: PAD token = 2 '</s>'
llama-cpp-python | llm_load_print_meta: LF token = 13 '<0x0A>'
llama-cpp-python | ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
llama-cpp-python | ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
llama-cpp-python | ggml_cuda_init: found 1 CUDA devices:
llama-cpp-python | Device 0: NVIDIA GeForce RTX 2070, compute capability 7.5, VMM: yes
llama-cpp-python | llm_load_tensors: ggml ctx size = 0.30 MiB
llama-cpp-python | llm_load_tensors: offloading 32 repeating layers to GPU
llama-cpp-python | llm_load_tensors: offloading non-repeating layers to GPU
llama-cpp-python | llm_load_tensors: offloaded 33/33 layers to GPU
llama-cpp-python | llm_load_tensors: CPU buffer size = 70.32 MiB
llama-cpp-python | llm_load_tensors: CUDA0 buffer size = 3847.57 MiB
llama-cpp-python | ..................................................................................................
llama-cpp-python | llama_new_context_with_model: n_ctx = 4096
llama-cpp-python | llama_new_context_with_model: n_batch = 192
llama-cpp-python | llama_new_context_with_model: n_ubatch = 192
llama-cpp-python | llama_new_context_with_model: freq_base = 1000000.0
llama-cpp-python | llama_new_context_with_model: freq_scale = 1
llama-cpp-python | llama_kv_cache_init: CUDA0 KV buffer size = 512.00 MiB
llama-cpp-python | llama_new_context_with_model: KV self size = 512.00 MiB, K (f16): 256.00 MiB, V (f16): 256.00 MiB
llama-cpp-python | llama_new_context_with_model: CUDA_Host output buffer size = 0.14 MiB
llama-cpp-python | llama_new_context_with_model: CUDA0 compute buffer size = 111.00 MiB
llama-cpp-python | llama_new_context_with_model: CUDA_Host compute buffer size = 6.00 MiB
llama-cpp-python | llama_new_context_with_model: graph nodes = 1030
llama-cpp-python | llama_new_context_with_model: graph splits = 2
llama-cpp-python | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 |
llama-cpp-python | Model metadata: {'tokenizer.chat_template': '{% for message in messages %}\n{% if message[\'role\'] == \'user\' or message[\'role\'] == \'system\' %}\n{{ \'<|from|>\' + message[\'role\'] + \'\n<|recipient|>all\n<|content|>\' + message[\'content\'] + \'\n\' }}{% elif message[\'role\'] == \'tool\' %}\n{{ \'<|from|>\' + message[\'name\'] + \'\n<|recipient|>all\n<|content|>\' + message[\'content\'] + \'\n\' }}{% else %}\n{% set contain_content=\'no\'%}\n{% if message[\'content\'] is not none %}\n{{ \'<|from|>assistant\n<|recipient|>all\n<|content|>\' + message[\'content\'] }}{% set contain_content=\'yes\'%}\n{% endif %}\n{% if \'tool_calls\' in message and message[\'tool_calls\'] is not none %}\n{% for tool_call in message[\'tool_calls\'] %}\n{% set prompt=\'<|from|>assistant\n<|recipient|>\' + tool_call[\'function\'][\'name\'] + \'\n<|content|>\' + tool_call[\'function\'][\'arguments\'] %}\n{% if loop.index == 1 and contain_content == "no" %}\n{{ prompt }}{% else %}\n{{ \'\n\' + prompt}}{% endif %}\n{% endfor %}\n{% endif %}\n{{ \'<|stop|>\n\' }}{% endif %}\n{% endfor %}\n{% if add_generation_prompt %}{{ \'<|from|>assistant\n<|recipient|>\' }}{% endif %}', 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.ggml.padding_token_id': '2', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.architecture': 'llama', 'llama.rope.freq_base': '1000000.000000', 'llama.context_length': '32768', 'general.name': '.', 'llama.vocab_size': '32004', 'general.file_type': '2', 'tokenizer.ggml.add_bos_token': 'true', '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': '1', 'llama.attention.head_count': '32', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8'}
llama-cpp-python | INFO: Started server process [27]
llama-cpp-python | INFO: Waiting for application startup.
llama-cpp-python | INFO: Application startup complete.
llama-cpp-python | INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
Edit:
This is when I asked it “How old is the Earth” or “Wie alt ist die Erde” in German.
llama-cpp-python | llama_print_timings: load time = 281.85 ms
llama-cpp-python | llama_print_timings: sample time = 0.34 ms / 3 runs ( 0.11 ms per token, 8771.93 tokens per second)
llama-cpp-python | llama_print_timings: prompt eval time = 2562.53 ms / 2476 tokens ( 1.03 ms per token, 966.23 tokens per second)
llama-cpp-python | llama_print_timings: eval time = 51.68 ms / 2 runs ( 25.84 ms per token, 38.70 tokens per second)
llama-cpp-python | llama_print_timings: total time = 6041.46 ms / 2478 tokens
llama-cpp-python | Llama.generate: prefix-match hit
llama-cpp-python |
llama-cpp-python | llama_print_timings: load time = 281.85 ms
llama-cpp-python | llama_print_timings: sample time = 7.46 ms / 67 runs ( 0.11 ms per token, 8976.42 tokens per second)
llama-cpp-python | llama_print_timings: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second)
llama-cpp-python | llama_print_timings: eval time = 1227.71 ms / 67 runs ( 18.32 ms per token, 54.57 tokens per second)
llama-cpp-python | llama_print_timings: total time = 2168.88 ms / 68 tokens
llama-cpp-python | INFO: 192.168.178.10:54778 - "POST /v1/chat/completions HTTP/1.1" 200 OK