Spaces:
Sleeping
Sleeping
init
Browse files- Dockerfile +11 -0
- README.md +11 -11
- main.py +76 -0
- requirements.txt +3 -0
Dockerfile
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FROM python:3.9
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WORKDIR /code
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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COPY . .
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CMD ["gunicorn", "-b", "0.0.0.0:7860", "main:app"]
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README.md
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---
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title:
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emoji:
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colorFrom:
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colorTo:
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sdk: docker
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Huggingface Chat API Adapter
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emoji: 💻
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colorFrom: yellow
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colorTo: indigo
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sdk: docker
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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main.py
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import random
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import requests
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from flask import Flask, request, Response, stream_with_context, render_template_string
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app = Flask(__name__)
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@app.route('/', methods=['GET'])
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def index():
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template = '''
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<html>
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<head>
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<title>Huggingface Chat API Adapter</title>
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</head>
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<body>
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<h1>Huggingface Chat API Adapter</h1>
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[Introduction]<br>
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When using Huggingface's Serverless Inference API for a conversation, by default 100 new tokens are output and a cache is used.<br>
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This API changes these two default settings, and other parameters are consistent with the official API.<br>
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<br>
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[How to use]<br>
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1. <a target="_blank" href="https://huggingface.co/settings/tokens/new">Create a token</a> with the "Make calls to the serverless Inference API" permission as an API key.<br>
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2. Set the Base URL of the OpenAI compatible client to "https://tastypear-sia-chat-adapter.hf.space/api".<br>
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3. Use the full name of the model (e.g. mistralai/Mistral-Nemo-Instruct-2407)<br>
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<br>
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[Supported models]<br>
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Most of the available models can be found <a target="_blank" href="https://huggingface.co/models?inference=warm&other=text-generation-inference">HERE</a>.<br>
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Some "cold" models may also be supported (e.g. meta-llama/Meta-Llama-3.1-405B-Instruct), please test it yourself.<br>
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Some models require a token created by a PRO user to use.<br>
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<br>
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[Avoid reaching the call limit]<br>
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If you have multiple tokens, you can connect them with a semicolon (";") and the API will use a random one (e.g. "hf_aaaa;hf_bbbb;hf_...")<br>
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</body>
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</html>
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'''
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return render_template_string(template)
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@app.route('/api/v1/chat/completions', methods=['POST'])
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def proxy():
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headers = dict(request.headers)
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headers.pop('Host', None)
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headers.pop('Content-Length', None)
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keys = request.headers['Authorization'].split(' ')[1].split(';')
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headers['Authorization'] = f'Bearer {random.choice(keys)}'
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headers['X-Use-Cache'] = 'false'
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json_data = request.get_json()
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model = json_data['model']
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chat_api = f"https://api-inference.huggingface.co/models/{model}/v1/chat/completions"
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# Try to use the largest ctx
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if not 'max_tokens' in json_data:
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json_data['max_tokens'] = 2**32-1
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json_data['json_mode'] = True
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info = requests.post(chat_api, json=request.json, headers=headers, stream=False).text
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max_ctx = int(info.split("<= ")[1].split(".")[0])
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inputs = int(info.split("Given: ")[1].split("`")[0])
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json_data['json_mode'] = False
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json_data['max_tokens'] = max_ctx - inputs - 1
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if not 'seed' in json_data:
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json_data['seed'] = random.randint(1,2**32)
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def generate():
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with requests.post(chat_api, json=request.json, headers=headers, stream=True) as resp:
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for chunk in resp.iter_content(chunk_size=1024):
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if chunk:
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yield chunk
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return Response(stream_with_context(generate()), content_type='text/event-stream')
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#import gevent.pywsgi
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#from gevent import monkey;monkey.patch_all()
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if __name__ == "__main__":
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app.run(debug=True)
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# gevent.pywsgi.WSGIServer((args.host, args.port), app).serve_forever()
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requirements.txt
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requests
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flask
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gunicorn
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