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--- |
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license: apache-2.0 |
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--- |
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To use GGUF locally, first download GGUF models locally. |
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One option you can use is to use `huggingface-cli`. To download `huggingface-cli` please follow tutorials in https://huggingface.co./docs/huggingface_hub/main/en/guides/cli. |
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Then, do command (also replace `{QUANTIZATION_METHOD}` with one of your chosen quantization method) |
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```bash |
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huggingface-cli download gorilla-llm/gorilla-openfunctions-v1-gguf gorilla-openfunctions-v1-{QUANTIZATION_METHOD}.gguf --local-dir gorilla-openfunctions-v1-GGUF |
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``` |
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It will store the QUANTIZATION_METHOD GGUF file to your local directory, `gorilla-openfunctions-v1-GGUF`. |
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We support QUANTIZATION_METHOD = {`q2_K`, `q3K_S`, `q3K_M`, `q3K_L`, `q4K_S`, `q4K_M`, `q5K_S`, `q5K_M`, `q6K`}. |
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Please let us know what other quantization methods you would like us to include! |
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Then, you can run the following example script to see an example of local inference. Fill in `YOUR_DIRECTORY` in this code snippet. This script is adapted from https://github.com/abetlen/llama-cpp-python and https://github.com/ShishirPatil/gorilla/tree/main/openfunctions |
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```python |
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from llama_cpp import Llama |
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import json |
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llm = Llama(model_path="YOUR_DIRECTORY/gorilla-openfunctions-v1-GGUF/gorilla-openfunctions-v1-q2_K.gguf", n_threads=8, n_gpu_layers=35) |
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def get_prompt(user_query: str, functions: list = []) -> str: |
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""" |
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Generates a conversation prompt based on the user's query and a list of functions. |
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Parameters: |
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- user_query (str): The user's query. |
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- functions (list): A list of functions to include in the prompt. |
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Returns: |
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- str: The formatted conversation prompt. |
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""" |
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system = "You are an AI programming assistant, utilizing the Gorilla LLM model, developed by Gorilla LLM, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer." |
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if len(functions) == 0: |
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return f"{system}\n### Instruction: <<question>> {user_query}\n### Response: " |
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functions_string = json.dumps(functions) |
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return f"{system}\n### Instruction: <<function>>{functions_string}\n<<question>>{user_query}\n### Response: " |
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query = "What's the weather like in the two cities of Boston and San Francisco?" |
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functions = [ |
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{ |
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"name": "get_current_weather", |
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"description": "Get the current weather in a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": { |
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"type": "string", |
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"description": "The city and state, e.g. San Francisco, CA", |
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}, |
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, |
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}, |
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"required": ["location"], |
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}, |
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} |
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] |
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user_prompt = get_prompt(query, functions) |
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output = llm(user_prompt, |
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max_tokens=512, # Generate up to 512 tokens |
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stop=["<|EOT|>"], |
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echo=True # Whether to echo the prompt |
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) |
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print("Output: ", output) |
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``` |
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The expected output of successfully running this script is the following (tested on March 3, 2024) |
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``` |
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❯ python quantized_inference.py |
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llama_model_loader: loaded meta data with 22 key-value pairs and 273 tensors from /Users/charliecheng-jieji/Downloads/codebase/quantized_eval/gorilla-openfunctions-v1-GGUF/gorilla-openfunctions-v0-q2_K.gguf (version GGUF V3 (latest)) |
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llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. |
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llama_model_loader: - kv 0: general.architecture str = llama |
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llama_model_loader: - kv 1: general.name str = LLaMA v0 |
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llama_model_loader: - kv 2: llama.context_length u32 = 4096 |
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llama_model_loader: - kv 3: llama.embedding_length u32 = 4096 |
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llama_model_loader: - kv 4: llama.block_count u32 = 30 |
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llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008 |
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llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 |
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llama_model_loader: - kv 7: llama.attention.head_count u32 = 32 |
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llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32 |
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llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001 |
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llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000 |
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llama_model_loader: - kv 11: general.file_type u32 = 10 |
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llama_model_loader: - kv 12: tokenizer.ggml.model str = gpt2 |
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llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,102400] = ["!", "\"", "#", "$", "%", "&", "'", ... |
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llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,102400] = [0.000000, 0.000000, 0.000000, 0.0000... |
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llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,102400] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... |
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llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,99757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e... |
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llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 100000 |
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llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 100015 |
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llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 100001 |
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llama_model_loader: - kv 20: tokenizer.chat_template str = {% if not add_generation_prompt is de... |
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llama_model_loader: - kv 21: general.quantization_version u32 = 2 |
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llama_model_loader: - type f32: 61 tensors |
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llama_model_loader: - type q2_K: 121 tensors |
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llama_model_loader: - type q3_K: 90 tensors |
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llama_model_loader: - type q6_K: 1 tensors |
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llm_load_vocab: mismatch in special tokens definition ( 2387/102400 vs 2400/102400 ). |
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llm_load_print_meta: format = GGUF V3 (latest) |
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llm_load_print_meta: arch = llama |
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llm_load_print_meta: vocab type = BPE |
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llm_load_print_meta: n_vocab = 102400 |
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llm_load_print_meta: n_merges = 99757 |
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llm_load_print_meta: n_ctx_train = 4096 |
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llm_load_print_meta: n_embd = 4096 |
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llm_load_print_meta: n_head = 32 |
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llm_load_print_meta: n_head_kv = 32 |
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llm_load_print_meta: n_layer = 30 |
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llm_load_print_meta: n_rot = 128 |
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llm_load_print_meta: n_embd_head_k = 128 |
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llm_load_print_meta: n_embd_head_v = 128 |
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llm_load_print_meta: n_gqa = 1 |
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llm_load_print_meta: n_embd_k_gqa = 4096 |
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llm_load_print_meta: n_embd_v_gqa = 4096 |
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llm_load_print_meta: f_norm_eps = 0.0e+00 |
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llm_load_print_meta: f_norm_rms_eps = 1.0e-06 |
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llm_load_print_meta: f_clamp_kqv = 0.0e+00 |
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llm_load_print_meta: f_max_alibi_bias = 0.0e+00 |
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llm_load_print_meta: n_ff = 11008 |
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llm_load_print_meta: n_expert = 0 |
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llm_load_print_meta: n_expert_used = 0 |
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llm_load_print_meta: pooling type = 0 |
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llm_load_print_meta: rope type = 0 |
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llm_load_print_meta: rope scaling = linear |
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llm_load_print_meta: freq_base_train = 10000.0 |
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llm_load_print_meta: freq_scale_train = 1 |
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llm_load_print_meta: n_yarn_orig_ctx = 4096 |
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llm_load_print_meta: rope_finetuned = unknown |
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llm_load_print_meta: model type = ?B |
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llm_load_print_meta: model ftype = Q2_K - Medium |
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llm_load_print_meta: model params = 6.91 B |
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llm_load_print_meta: model size = 2.53 GiB (3.14 BPW) |
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llm_load_print_meta: general.name = LLaMA v2 |
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llm_load_print_meta: BOS token = 100000 '<｜begin▁of▁sentence｜>' |
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llm_load_print_meta: EOS token = 100015 '<|EOT|>' |
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llm_load_print_meta: PAD token = 100001 '<｜end▁of▁sentence｜>' |
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llm_load_print_meta: LF token = 126 'Ä' |
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llm_load_tensors: ggml ctx size = 0.21 MiB |
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ggml_backend_metal_buffer_from_ptr: allocated buffer, size = 2457.45 MiB, ( 2457.52 / 10922.67) |
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llm_load_tensors: offloading 30 repeating layers to GPU |
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llm_load_tensors: offloading non-repeating layers to GPU |
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llm_load_tensors: offloaded 31/31 layers to GPU |
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llm_load_tensors: CPU buffer size = 131.25 MiB |
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llm_load_tensors: Metal buffer size = 2457.45 MiB |
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..................................................................................... |
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llama_new_context_with_model: n_ctx = 512 |
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llama_new_context_with_model: freq_base = 10000.0 |
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llama_new_context_with_model: freq_scale = 1 |
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ggml_metal_init: allocating |
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ggml_metal_init: found device: Apple M1 |
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ggml_metal_init: picking default device: Apple M1 |
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ggml_metal_init: default.metallib not found, loading from source |
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ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil |
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ggml_metal_init: loading '/Users/charliecheng-jieji/miniconda3/envs/public-api/lib/python3.12/site-packages/llama_cpp/ggml-metal.metal' |
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ggml_metal_init: GPU name: Apple M1 |
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ggml_metal_init: GPU family: MTLGPUFamilyApple7 (1007) |
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ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003) |
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ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001) |
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ggml_metal_init: simdgroup reduction support = true |
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ggml_metal_init: simdgroup matrix mul. support = true |
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ggml_metal_init: hasUnifiedMemory = true |
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ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB |
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ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 240.00 MiB, ( 2699.33 / 10922.67) |
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llama_kv_cache_init: Metal KV buffer size = 240.00 MiB |
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llama_new_context_with_model: KV self size = 240.00 MiB, K (f16): 120.00 MiB, V (f16): 120.00 MiB |
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llama_new_context_with_model: CPU input buffer size = 10.01 MiB |
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ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 208.00 MiB, ( 2907.33 / 10922.67) |
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llama_new_context_with_model: Metal compute buffer size = 208.00 MiB |
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llama_new_context_with_model: CPU compute buffer size = 8.00 MiB |
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llama_new_context_with_model: graph splits (measure): 2 |
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AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | |
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Model metadata: {'general.quantization_version': '2', 'tokenizer.chat_template': "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}", 'tokenizer.ggml.padding_token_id': '100001', 'tokenizer.ggml.eos_token_id': '100015', 'tokenizer.ggml.bos_token_id': '100000', 'tokenizer.ggml.model': 'gpt2', 'llama.attention.head_count_kv': '32', 'llama.context_length': '4096', 'llama.attention.head_count': '32', 'llama.rope.freq_base': '10000.000000', 'llama.rope.dimension_count': '128', 'general.file_type': '10', 'llama.feed_forward_length': '11008', 'llama.embedding_length': '4096', 'llama.block_count': '30', 'general.architecture': 'llama', 'llama.attention.layer_norm_rms_epsilon': '0.000001', 'general.name': 'LLaMA v2'} |
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Using gguf chat template: {% if not add_generation_prompt is defined %} |
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{% set add_generation_prompt = false %} |
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{% endif %} |
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{%- set ns = namespace(found=false) -%} |
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{%- for message in messages -%} |
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{%- if message['role'] == 'system' -%} |
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{%- set ns.found = true -%} |
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{%- endif -%} |
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{%- endfor -%} |
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{{bos_token}}{%- if not ns.found -%} |
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{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n'}} |
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{%- endif %} |
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{%- for message in messages %} |
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{%- if message['role'] == 'system' %} |
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{{ message['content'] }} |
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{%- else %} |
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{%- if message['role'] == 'user' %} |
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{{'### Instruction:\n' + message['content'] + '\n'}} |
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{%- else %} |
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{{'### Response:\n' + message['content'] + '\n<|EOT|>\n'}} |
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{%- endif %} |
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{%- endif %} |
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{%- endfor %} |
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{% if add_generation_prompt %} |
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{{'### Response:'}} |
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{% endif %} |
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Using chat eos_token: <|EOT|> |
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Using chat bos_token: <｜begin▁of▁sentence｜> |
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llama_print_timings: load time = 1890.11 ms |
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llama_print_timings: sample time = 23.48 ms / 40 runs ( 0.59 ms per token, 1703.94 tokens per second) |
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llama_print_timings: prompt eval time = 1889.91 ms / 181 tokens ( 10.44 ms per token, 95.77 tokens per second) |
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llama_print_timings: eval time = 2728.54 ms / 39 runs ( 69.96 ms per token, 14.29 tokens per second) |
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llama_print_timings: total time = 5162.12 ms / 220 tokens |
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``` |
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<code> |
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Output: {'id': 'cmpl-0679223d-578f-42be-bbce-0e307faddd28', 'object': 'text_completion', 'created': 1709525244, 'model': '/Users/charliecheng-jieji/Downloads/codebase/quantized_eval/gorilla-openfunctions-v1-GGUF/gorilla-openfunctions-v1-q2_K.gguf', 'choices': [{'text': 'You are an AI programming assistant, utilizing the Gorilla LLM model, developed by Gorilla LLM, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.\n### Instruction: <<function>>[{"name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}, "required": ["location"]}}]\n<<question>>What\'s the weather like in the two cities of Boston and San Francisco?\n### Response: <<function>>get_current_weather(location=\'Boston\', unit=\'fahrenheit\')<<function>>get_current_weather(location=\'San Francisco\', unit=\'fahrenheit\')', 'index': 0, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 181, 'completion_tokens': 39, 'total_tokens': 220}} |
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</code> |
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