File size: 4,269 Bytes
e2a9bea 9ccc711 e2a9bea 75c0d73 e2a9bea 07cc15a 8593e5c e2a9bea 8593e5c e2a9bea 75c0d73 e2a9bea 13e2bc6 e2a9bea 13e2bc6 e2a9bea 13e2bc6 e2a9bea 8593e5c e2a9bea 8593e5c e2a9bea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
---
title: HF LLM API
emoji: ☪️
colorFrom: gray
colorTo: gray
sdk: docker
app_port: 23333
---
## HF-LLM-API
Huggingface LLM Inference API in OpenAI message format.
# Original Project link: https://github.com/Hansimov/HF-LLM-API
## Features
- Available Models (2024/04/20):
- `mistral-7b`, `mixtral-8x7b`, `nous-mixtral-8x7b`, `gemma-7b`, `command-r-plus`, `llama3-70b`, `zephyr-141b`, `gpt-3.5-turbo`
- Adaptive prompt templates for different models
- Support OpenAI API format
- Enable api endpoint via official `openai-python` package
- Support both stream and no-stream response
- Support API Key via both HTTP auth header and env variable
- Docker deployment
## 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:1.1.3 . --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:1.1.3
# with proxy
sudo docker run -p 23333:23333 --env http_proxy="http://<server>:<port>" hf-llm-api:1.1.3
```
## API Usage
### Using `openai-python`
See: [`examples/chat_with_openai.py`](https://github.com/Niansuh/HF-LLM-API/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"
# Your own HF_TOKEN
api_key = "hf_xxxxxxxxxxxxxxxx"
# use below as non-auth user
# api_key = "sk-xxx"
client = OpenAI(base_url=base_url, api_key=api_key)
response = client.chat.completions.create(
model="nous-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/Niansuh/HF-LLM-API/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"
# Your own HF_TOKEN
api_key = "hf_xxxxxxxxxxxxxxxx"
# use below as non-auth user
# api_key = "sk-xxx"
requests_headers = {}
requests_payload = {
"model": "nous-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()
```
|