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| import fastapi | |
| import json | |
| import uvicorn | |
| from fastapi import HTTPException | |
| from fastapi.responses import HTMLResponse | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from sse_starlette.sse import EventSourceResponse | |
| from starlette.responses import StreamingResponse | |
| from ctransformers import AutoModelForCausalLM | |
| from pydantic import BaseModel | |
| from typing import List, Dict, Any, Generator | |
| llm = AutoModelForCausalLM.from_pretrained("TheBloke/falcon-40b-instruct-GGML", model_file="falcon40b-instruct.ggmlv3.q2_K.bin", | |
| model_type="falcon", threads=8) | |
| app = fastapi.FastAPI(title="🦅Falcon 40B GGML (ggmlv3.q2_K)🦅") | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| class ChatCompletionRequestV0(BaseModel): | |
| prompt: str | |
| class Message(BaseModel): | |
| role: str | |
| content: str | |
| class ChatCompletionRequest(BaseModel): | |
| messages: List[Message] | |
| max_tokens: int = 250 | |
| async def completion(request: ChatCompletionRequestV0, response_mode=None): | |
| response = llm(request.prompt) | |
| return response | |
| async def chat(request: ChatCompletionRequest): | |
| combined_messages = ' '.join([message.content for message in request.messages]) | |
| tokens = llm.tokenize(combined_messages) | |
| try: | |
| chat_chunks = llm.generate(tokens) | |
| except Exception as e: | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| async def format_response(chat_chunks: Generator) -> Any: | |
| for chat_chunk in chat_chunks: | |
| response = { | |
| 'choices': [ | |
| { | |
| 'message': { | |
| 'role': 'system', | |
| 'content': llm.detokenize(chat_chunk) | |
| }, | |
| 'finish_reason': 'stop' if llm.detokenize(chat_chunk) == "[DONE]" else 'unknown' | |
| } | |
| ] | |
| } | |
| yield f"data: {json.dumps(response)}\n\n" | |
| yield "event: done\ndata: {}\n\n" | |
| return StreamingResponse(format_response(chat_chunks), media_type="text/event-stream") | |
| async def chat(request: ChatCompletionRequestV0, response_mode=None): | |
| tokens = llm.tokenize(request.prompt) | |
| async def server_sent_events(chat_chunks, llm): | |
| for chat_chunk in llm.generate(chat_chunks): | |
| yield dict(data=json.dumps(llm.detokenize(chat_chunk))) | |
| yield dict(data="[DONE]") | |
| return EventSourceResponse(server_sent_events(tokens, llm)) | |
| if __name__ == "__main__": | |
| uvicorn.run(app, host="0.0.0.0", port=8000) |