--- title: HF LLM API COLLECTION emoji: ☯️ colorFrom: gray colorTo: gray sdk: docker app_port: 23333 --- ## HF-LLM-API-COLLECTION Huggingface LLM Inference API in OpenAI message format. ## Features ✅ Implemented: - Support Models - `mixtral-8x7b`, `mistral-7b` - Support OpenAI API format - Can use api endpoint via official `openai-python` package - Stream response - Docker deployment 🔨 In progress: - [x] Support more models ## Run API service ### Run in Command Line **Install dependencies:** ```bash # pipreqs . --force --mode no-pin pip install -r requirements.txt ``` **Run API:** ```bash python -m apis.chat_api ``` ## Run via Docker **Docker build:** ```bash sudo docker build -t hf-llm-api:1.0 . --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy ``` **Docker run:** ```bash # no proxy sudo docker run -p 23333:23333 hf-llm-api:1.0 # with proxy sudo docker run -p 23333:23333 --env http_proxy="http://:" hf-llm-api:1.0 ``` ## API Usage ### Using `openai-python` See: [examples/chat_with_openai.py](https://github.com/ruslanmv/hf-llm-api-collection/blob/main/examples/chat_with_openai.py) ```py from openai import OpenAI # If runnning this service with proxy, you might need to unset `http(s)_proxy`. base_url = "http://127.0.0.1:23333" api_key = "sk-xxxxx" client = OpenAI(base_url=base_url, api_key=api_key) response = client.chat.completions.create( model="mixtral-8x7b", messages=[ { "role": "user", "content": "what is your model", } ], stream=True, ) for chunk in response: if chunk.choices[0].delta.content is not None: print(chunk.choices[0].delta.content, end="", flush=True) elif chunk.choices[0].finish_reason == "stop": print() else: pass ``` ### Using post requests See: [examples/chat_with_post.py](https://github.com/ruslanmv/hf-llm-api-collection/blob/main/examples/chat_with_post.py) ```py import ast import httpx import json import re # If runnning this service with proxy, you might need to unset `http(s)_proxy`. chat_api = "http://127.0.0.1:23333" api_key = "sk-xxxxx" requests_headers = {} requests_payload = { "model": "mixtral-8x7b", "messages": [ { "role": "user", "content": "what is your model", } ], "stream": True, } with httpx.stream( "POST", chat_api + "/chat/completions", headers=requests_headers, json=requests_payload, timeout=httpx.Timeout(connect=20, read=60, write=20, pool=None), ) as response: # https://docs.aiohttp.org/en/stable/streams.html # https://github.com/openai/openai-cookbook/blob/main/examples/How_to_stream_completions.ipynb response_content = "" for line in response.iter_lines(): remove_patterns = [r"^\s*data:\s*", r"^\s*\[DONE\]\s*"] for pattern in remove_patterns: line = re.sub(pattern, "", line).strip() if line: try: line_data = json.loads(line) except Exception as e: try: line_data = ast.literal_eval(line) except: print(f"Error: {line}") raise e # print(f"line: {line_data}") delta_data = line_data["choices"][0]["delta"] finish_reason = line_data["choices"][0]["finish_reason"] if "role" in delta_data: role = delta_data["role"] if "content" in delta_data: delta_content = delta_data["content"] response_content += delta_content print(delta_content, end="", flush=True) if finish_reason == "stop": print() ```