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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/WizardLM-Uncensored-Falcon-40B-GGML", model_file="wizardlm-uncensored-falcon-40b.ggccv1.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
@app.post("/v1/completions")
async def completion(request: ChatCompletionRequestV0, response_mode=None):
response = llm(request.prompt)
return response
@app.post("/v1/chat/completions")
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")
@app.post("/v0/chat/completions")
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) |