Spaces:
Runtime error
Runtime error
File size: 2,073 Bytes
028f06a 8087bbe 028f06a 23cc740 56421a8 028f06a b4957d9 028f06a 8087bbe 028f06a 8087bbe b4957d9 8087bbe b4957d9 8087bbe b4957d9 8087bbe 028f06a 56421a8 028f06a 7860df6 56421a8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import os
import torch
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel, PeftConfig
import uvicorn
from huggingface_hub import login
# Authenticate with Hugging Face Hub
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN:
login(token=HF_TOKEN)
else:
raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
# Define a Pydantic model for request validation
class Query(BaseModel):
text: str
app = FastAPI(title="Financial Chatbot API")
# Load the base model
base_model_name = "meta-llama/Llama-3.2-3B" # Update if using a different base model
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
device_map="auto",
trust_remote_code=True
)
# Load adapter from your checkpoint with a workaround for 'eva_config'
peft_model_id = "Phoenix21/llama-3-2-3b-finetuned-finance_checkpoint2"
# Load the PEFT configuration first
peft_config = PeftConfig.from_pretrained(peft_model_id)
# Remove 'eva_config' if it exists in the configuration
peft_config_dict = peft_config.to_dict()
if "eva_config" in peft_config_dict:
peft_config_dict.pop("eva_config")
peft_config = PeftConfig.from_dict(peft_config_dict)
# Load the adapter using the filtered configuration
model = PeftModel.from_pretrained(model, peft_model_id, config=peft_config)
# Load tokenizer from the base model
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
# Create a text-generation pipeline
chat_pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=256,
temperature=0.7,
top_p=0.95,
)
@app.post("/generate")
def generate(query: Query):
prompt = f"Question: {query.text}\nAnswer: "
response = chat_pipe(prompt)[0]["generated_text"]
return {"response": response}
if __name__ == "__main__":
port = int(os.environ.get("PORT", 7860))
uvicorn.run(app, host="0.0.0.0", port=port)
|