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from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import os
import requests
# from langchain.llms.huggingface_pipeline import HuggingFacePipeline
# API_URL = "/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2Fmistralai%2FMistral-7B-v0.1%26quot%3B%3C%2Fspan%3E
# headers = {"Authorization": f"Bearer {key}"}
# def query(payload):
# response = requests.post(API_URL, headers=headers, json=payload)
# return response.json()
# def LLM(llm_name, length):
# print(llm_name)
# tokenizer = AutoTokenizer.from_pretrained(llm_name)
# model = AutoModelForCausalLM.from_pretrained(llm_name,
# trust_remote_code=True,
# device_map="auto",
# load_in_8bit=True)
# pipe = pipeline("text-generation",
# model=model,
# tokenizer=tokenizer,
# max_length=length,
# do_sample=True,
# top_p=0.95,
# repetition_penalty=1.2,
# )
# return pipe
tokenizer = AutoTokenizer.from_pretrained('replit/replit-code-v1_5-3b', trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1_5-3b', trust_remote_code=True)
x = tokenizer.encode('def fibonacci(n): ', return_tensors='pt')
y = model.generate(x, max_length=100, do_sample=True, top_p=0.95, top_k=4, temperature=0.2, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
# decoding
generated_code = tokenizer.decode(y[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(generated_code)
pipe = generated_code
# tokenizer = AutoTokenizer.from_pretrained("WizardLM/WizardCoder-1B-V1.0")
# base_model = AutoModelForCausalLM.from_pretrained("WizardLM/WizardCoder-1B-V1.0")
# Mistral 7B
# mistral_llm = LLM("mistralai/Mistral-7B-v0.1",30000)
mistral_llm = pipe
# WizardCoder 13B
# wizard_llm = LLM("WizardLM/WizardCoder-Python-13B-V1.0",8000)
wizard_llm = pipe
# hf_llm = HuggingFacePipeline(pipeline=pipe)
def ask_model(model, prompt):
if(model == 'mistral'):
return mistral_llm(prompt)
if(model == 'wizard'):
return wizard_llm(prompt)
key = os.environ.get("huggingface_key")
openai_api_key = os.environ.get("openai_key")
app = FastAPI(openapi_url="/api/v1/LLM/openapi.json", docs_url="/api/v1/LLM/docs")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
allow_credentials=True,
)
@app.get("/")
def root():
return {"message": "R&D LLM API"}
# @app.get("/get")
# def get():
# result = pipe("name 5 programming languages",do_sample=False)
# print(result)
# return {"message": result}
@app.post("/ask_llm")
async def ask_llm_endpoint(model:str, prompt: str):
result = ask_model(model,prompt)
return {"result": result}
# APIs
# @app.post("/ask_HFAPI")
# def ask_HFAPI_endpoint(prompt: str):
# result = query(prompt)
# return {"result": result}
from langchain.llms import OpenAI
llm = OpenAI(model_name="text-davinci-003", temperature=0.5, openai_api_key=openai_api_key)
@app.post("/ask_GPT")
def ask_GPT_endpoint(prompt: str):
result = llm(prompt)
return {"result": result} |