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Duplicate from gsaivinay/Llama-2-13B-GGML-server
7438863
import json
from typing import List
import fastapi
import markdown
import uvicorn
from ctransformers import AutoModelForCausalLM
from fastapi import HTTPException
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from sse_starlette.sse import EventSourceResponse
from pydantic import BaseModel, Field
from typing_extensions import Literal
from dialogue import DialogueTemplate
# llm = AutoModelForCausalLM.from_pretrained("gsaivinay/airoboros-13B-gpt4-1.3-GGML",
# model_file="airoboros-13b-gpt4-1.3.ggmlv3.q4_1.bin",
# model_type="llama")
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-13B-chat-GGML",
model_file="llama-2-13b-chat.ggmlv3.q2_K.bin",
model_type="llama")
app = fastapi.FastAPI(title="Starchat Beta")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
async def index():
with open("README.md", "r", encoding="utf-8") as readme_file:
md_template_string = readme_file.read()
html_content = markdown.markdown(md_template_string)
return HTMLResponse(content=html_content, status_code=200)
@app.get("/stream")
async def chat(prompt = "<|user|> Write an express server with server sent events. <|assistant|>"):
tokens = llm.tokenize(prompt)
async def server_sent_events(chat_chunks, llm):
yield prompt
for chat_chunk in llm.generate(chat_chunks):
yield llm.detokenize(chat_chunk)
yield ""
return EventSourceResponse(server_sent_events(tokens, llm))
class ChatCompletionRequestMessage(BaseModel):
role: Literal["system", "user", "assistant"] = Field(
default="user", description="The role of the message."
)
content: str = Field(default="", description="The content of the message.")
class ChatCompletionRequest(BaseModel):
messages: List[ChatCompletionRequestMessage] = Field(
default=[], description="A list of messages to generate completions for."
)
system_message = "Below is a conversation between a human user and a helpful AI coding assistant."
@app.post("/v1/chat/completions")
async def chat(request: ChatCompletionRequest):
kwargs = request.dict()
dialogue_template = DialogueTemplate(
system=system_message, messages=kwargs['messages']
)
prompt = dialogue_template.get_inference_prompt()
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 EventSourceResponse(format_response(chat_chunks), media_type="text/event-stream")
@app.post("/v0/chat/completions")
async def chatV0(request: ChatCompletionRequest, response_mode=None):
kwargs = request.dict()
dialogue_template = DialogueTemplate(
system=system_message, messages=kwargs['messages']
)
prompt = dialogue_template.get_inference_prompt()
tokens = llm.tokenize(prompt)
async def server_sent_events(chat_chunks, llm):
for token in llm.generate(chat_chunks):
yield dict(data=llm.detokenize(token))
yield dict(data="[DONE]")
return EventSourceResponse(server_sent_events(tokens, llm))
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)