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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
class ModelInput(BaseModel):
prompt: str
max_new_tokens: int = 50
app = FastAPI()
# Define model paths
base_model_path = "HuggingFaceTB/SmolLM2-135M-Instruct"
adapter_path = "khurrameycon/SmolLM-135M-Instruct-qa_pairs_converted.json-25epochs"
try:
# First load the base model
print("Loading base model...")
model = AutoModelForCausalLM.from_pretrained(
base_model_path,
torch_dtype=torch.float16,
trust_remote_code=True,
device_map="auto"
)
# Load tokenizer from base model
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(base_model_path)
# Download adapter weights
print("Downloading adapter weights...")
adapter_path_local = snapshot_download(adapter_path)
# Load the safetensors file
print("Loading adapter weights...")
state_dict = load_file(f"{adapter_path_local}/adapter_model.safetensors")
# Load state dict into model
model.load_state_dict(state_dict, strict=False)
print("Model and adapter loaded successfully!")
except Exception as e:
print(f"Error during model loading: {e}")
raise
def generate_response(model, tokenizer, instruction, max_new_tokens=128):
"""Generate a response from the model based on an instruction."""
try:
messages = [{"role": "user", "content": instruction}]
input_text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=max_new_tokens,
temperature=0.2,
top_p=0.9,
do_sample=True,
)
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")
async def generate_text(input: ModelInput):
try:
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("/")
async def root():
return {"message": "Welcome to the Model API!"}
# //////////////////////////////////////////
# from fastapi import FastAPI, HTTPException
# from pydantic import BaseModel
# from transformers import AutoModelForCausalLM, AutoTokenizer, AutoAdapterModel
# import torch
# from huggingface_hub import snapshot_download
# class ModelInput(BaseModel):
# prompt: str
# max_new_tokens: int = 50
# app = FastAPI()
# # Define model paths
# base_model_path = "HuggingFaceTB/SmolLM2-135M-Instruct"
# adapter_path = "khurrameycon/SmolLM-135M-Instruct-qa_pairs_converted.json-25epochs"
# try:
# # First load the base model
# print("Loading base model...")
# model = AutoModelForCausalLM.from_pretrained(
# base_model_path,
# torch_dtype=torch.float16,
# trust_remote_code=True,
# device_map="auto"
# )
# # Load tokenizer from base model
# print("Loading tokenizer...")
# tokenizer = AutoTokenizer.from_pretrained(base_model_path)
# # Download adapter weights
# print("Downloading adapter weights...")
# adapter_path_local = snapshot_download(adapter_path)
# # Load the adapter model
# print("Loading adapter model...")
# adapter_model = AutoAdapterModel.from_pretrained(adapter_path_local, from_pt=True)
# # Combine the base model and adapter
# model = model.with_adapter(adapter_model)
# print("Model and adapter loaded successfully!")
# except Exception as e:
# print(f"Error during model loading: {e}")
# raise
# def generate_response(model, tokenizer, instruction, max_new_tokens=128):
# """Generate a response from the model based on an instruction."""
# try:
# messages = [{"role": "user", "content": instruction}]
# input_text = tokenizer.apply_chat_template(
# messages, tokenize=False, add_generation_prompt=True
# )
# inputs = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
# outputs = model.generate(
# inputs,
# max_new_tokens=max_new_tokens,
# temperature=0.2,
# top_p=0.9,
# do_sample=True,
# )
# 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")
# async def generate_text(input: ModelInput):
# try:
# 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("/")
# async def root():
# return {"message": "Welcome to the Model API!"}
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