--- license: apache-2.0 --- To use GGUF locally, first download GGUF models locally. 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. Then, do command (also replace `{QUANTIZATION_METHOD}` with one of your chosen quantization method) ```bash huggingface-cli download gorilla-llm/gorilla-openfunctions-v0-gguf gorilla-openfunctions-v0-{QUANTIZATION_METHOD}.gguf --local-dir gorilla-openfunctions-v0-GGUF ``` It will store the QUANTIZATION_METHOD GGUF file to your local directory, `gorilla-openfunctions-v0-GGUF`. We support QUANTIZATION_METHOD = {`q2_K`, `q3K_S`, `q3K_M`, `q3K_L`, `q4K_S`, `q4K_M`, `q5K_S`, `q5K_M`, `q6K`}. Please let us know what other quantization methods you would like us to include! 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 ```python from llama_cpp import Llama import json llm = Llama(model_path="YOUR_DIRECTORY/gorilla-openfunctions-v0-GGUF/gorilla-openfunctions-v0-q2_K.gguf", n_threads=8, n_gpu_layers=35) def get_prompt(user_query: str, functions: list = []) -> str: """ Generates a conversation prompt based on the user's query and a list of functions. Parameters: - user_query (str): The user's query. - functions (list): A list of functions to include in the prompt. Returns: - str: The formatted conversation prompt. """ 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." if len(functions) == 0: return f"{system}\n### Instruction: <> {user_query}\n### Response: " functions_string = json.dumps(functions) return f"{system}\n### Instruction: <>{functions_string}\n<>{user_query}\n### Response: " query = "What's the weather like in the two cities of Boston and San Francisco?" functions = [ { "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"], }, } ] user_prompt = get_prompt(query, functions) output = llm(user_prompt, max_tokens=512, # Generate up to 512 tokens stop=["<|EOT|>"], echo=True # Whether to echo the prompt ) print("Output: ", output) ``` The expected output of successfully running this script is the following (tested on March 3, 2024) ``` ❯ python quantized_inference.py llama_model_loader: loaded meta data with 22 key-value pairs and 273 tensors from /Users/charliecheng-jieji/Downloads/codebase/quantized_eval/gorilla-openfunctions-v0-GGUF/gorilla-openfunctions-v0-q2_K.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 = LLaMA v0 llama_model_loader: - kv 2: llama.context_length u32 = 4096 llama_model_loader: - kv 3: llama.embedding_length u32 = 4096 llama_model_loader: - kv 4: llama.block_count u32 = 30 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000 llama_model_loader: - kv 11: general.file_type u32 = 10 llama_model_loader: - kv 12: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,102400] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,102400] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,102400] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,99757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e... llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 100000 llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 100015 llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 100001 llama_model_loader: - kv 20: tokenizer.chat_template str = {% if not add_generation_prompt is de... llama_model_loader: - kv 21: general.quantization_version u32 = 2 llama_model_loader: - type f32: 61 tensors llama_model_loader: - type q2_K: 121 tensors llama_model_loader: - type q3_K: 90 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_vocab: mismatch in special tokens definition ( 2387/102400 vs 2400/102400 ). 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 = 102400 llm_load_print_meta: n_merges = 99757 llm_load_print_meta: n_ctx_train = 4096 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 32 llm_load_print_meta: n_layer = 30 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 = 1 llm_load_print_meta: n_embd_k_gqa = 4096 llm_load_print_meta: n_embd_v_gqa = 4096 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: n_ff = 11008 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 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 = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 4096 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: model type = ?B llm_load_print_meta: model ftype = Q2_K - Medium llm_load_print_meta: model params = 6.91 B llm_load_print_meta: model size = 2.53 GiB (3.14 BPW) llm_load_print_meta: general.name = LLaMA v2 llm_load_print_meta: BOS token = 100000 '<｜begin▁of▁sentence｜>' llm_load_print_meta: EOS token = 100015 '<|EOT|>' llm_load_print_meta: PAD token = 100001 '<｜end▁of▁sentence｜>' llm_load_print_meta: LF token = 126 'Ä' llm_load_tensors: ggml ctx size = 0.21 MiB ggml_backend_metal_buffer_from_ptr: allocated buffer, size = 2457.45 MiB, ( 2457.52 / 10922.67) llm_load_tensors: offloading 30 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 31/31 layers to GPU llm_load_tensors: CPU buffer size = 131.25 MiB llm_load_tensors: Metal buffer size = 2457.45 MiB ..................................................................................... llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 ggml_metal_init: allocating ggml_metal_init: found device: Apple M1 ggml_metal_init: picking default device: Apple M1 ggml_metal_init: default.metallib not found, loading from source ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil ggml_metal_init: loading '/Users/charliecheng-jieji/miniconda3/envs/public-api/lib/python3.12/site-packages/llama_cpp/ggml-metal.metal' ggml_metal_init: GPU name: Apple M1 ggml_metal_init: GPU family: MTLGPUFamilyApple7 (1007) ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003) ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001) ggml_metal_init: simdgroup reduction support = true ggml_metal_init: simdgroup matrix mul. support = true ggml_metal_init: hasUnifiedMemory = true ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 240.00 MiB, ( 2699.33 / 10922.67) llama_kv_cache_init: Metal KV buffer size = 240.00 MiB llama_new_context_with_model: KV self size = 240.00 MiB, K (f16): 120.00 MiB, V (f16): 120.00 MiB llama_new_context_with_model: CPU input buffer size = 10.01 MiB ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 208.00 MiB, ( 2907.33 / 10922.67) llama_new_context_with_model: Metal compute buffer size = 208.00 MiB llama_new_context_with_model: CPU compute buffer size = 8.00 MiB llama_new_context_with_model: graph splits (measure): 2 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 | 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'} Using gguf chat template: {% if not add_generation_prompt is defined %} {% set add_generation_prompt = false %} {% endif %} {%- set ns = namespace(found=false) -%} {%- for message in messages -%} {%- if message['role'] == 'system' -%} {%- set ns.found = true -%} {%- endif -%} {%- endfor -%} {{bos_token}}{%- if not ns.found -%} {{'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'}} {%- endif %} {%- for message in messages %} {%- if message['role'] == 'system' %} {{ message['content'] }} {%- else %} {%- if message['role'] == 'user' %} {{'### Instruction:\n' + message['content'] + '\n'}} {%- else %} {{'### Response:\n' + message['content'] + '\n<|EOT|>\n'}} {%- endif %} {%- endif %} {%- endfor %} {% if add_generation_prompt %} {{'### Response:'}} {% endif %} Using chat eos_token: <|EOT|> Using chat bos_token: <｜begin▁of▁sentence｜> llama_print_timings: load time = 1890.11 ms llama_print_timings: sample time = 23.48 ms / 40 runs ( 0.59 ms per token, 1703.94 tokens per second) llama_print_timings: prompt eval time = 1889.91 ms / 181 tokens ( 10.44 ms per token, 95.77 tokens per second) llama_print_timings: eval time = 2728.54 ms / 39 runs ( 69.96 ms per token, 14.29 tokens per second) llama_print_timings: total time = 5162.12 ms / 220 tokens ``` Output: {'id': 'cmpl-0679223d-578f-42be-bbce-0e307faddd28', 'object': 'text_completion', 'created': 1709525244, 'model': '/Users/charliecheng-jieji/Downloads/codebase/quantized_eval/gorilla-openfunctions-v0-GGUF/gorilla-openfunctions-v0-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: <>[{"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<>What\'s the weather like in the two cities of Boston and San Francisco?\n### Response: <>get_current_weather(location=\'Boston\', unit=\'fahrenheit\')<>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}}