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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from safetensors.torch import load_file
import torch
# Define the input schema
class ModelInput(BaseModel):
prompt: str
max_new_tokens: int = 50 # Optional: Defaults to 50 tokens
# Initialize FastAPI app
app = FastAPI()
# Load the base model and tokenizer
base_model_path = "HuggingFaceTB/SmolLM2-135M-Instruct" # Base model
adapter_weights_path = "https://huggingface.co/khurrameycon/SmolLM-135M-Instruct-qa_pairs_converted.json-25epochs/resolve/main/adapter_model.safetensors"
# Path to the adapter weights
tokenizer = AutoTokenizer.from_pretrained(base_model_path)
model = AutoModelForCausalLM.from_pretrained(base_model_path)
# Load the adapter weights
def load_adapter_weights(model, adapter_weights_path):
adapter_weights = load_file(adapter_weights_path)
model.load_state_dict(adapter_weights, strict=False) # Apply the weights
return model
# Apply adapter weights to the model
model = load_adapter_weights(model, adapter_weights_path)
# Ensure the model is in evaluation mode
model.eval()
# Initialize the pipeline
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Helper function to generate a response
def generate_response(model, tokenizer, instruction, max_new_tokens=128):
"""Generate a response from the model based on an instruction."""
try:
# Tokenize and generate the output
inputs = tokenizer(instruction, return_tensors="pt")
inputs = {key: value.to(model.device) for key, value in inputs.items()} # Move tensors to the model's device
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=0.7,
top_p=0.9,
do_sample=True,
)
# Decode the output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
except Exception as e:
raise ValueError(f"Error generating response: {e}")
@app.post("/generate")
def generate_text(input: ModelInput):
"""API endpoint to generate text."""
try:
# Call the helper function
response = generate_response(
model=model, tokenizer=tokenizer, instruction=input.prompt, max_new_tokens=input.max_new_tokens
)
return {"generated_text": response}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/")
def root():
return {"message": "Welcome to the Hugging Face Model API with Adapter Support!"}
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