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import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
from peft import PeftModel | |
import gradio as gr | |
# -------------------- | |
# Load Base Model and LoRA Adapter | |
# -------------------- | |
def load_model_and_adapter(): | |
base_model_name = "unsloth/Llama-3.2-3B-Instruct" # Replace with your base model name | |
adapter_repo = "Futuresony/future_ai_12_10_2024" # Your Hugging Face LoRA repo | |
# Load tokenizer and base model | |
tokenizer = AutoTokenizer.from_pretrained(base_model_name) | |
base_model = AutoModelForCausalLM.from_pretrained( | |
base_model_name, | |
torch_dtype=torch.float16, # Use float16 for efficiency if GPU is available | |
device_map="auto" # Automatically map to GPU or CPU | |
) | |
# Load LoRA adapter | |
model = PeftModel.from_pretrained(base_model, adapter_repo) | |
model.eval() # Set to evaluation mode | |
return tokenizer, model | |
# Load the model and tokenizer once | |
tokenizer, model = load_model_and_adapter() | |
# -------------------- | |
# Generate Response Function | |
# -------------------- | |
def respond( | |
message, | |
history: list[tuple[str, str]], | |
system_message, | |
max_tokens, | |
temperature, | |
top_p, | |
): | |
messages = [{"role": "system", "content": system_message}] | |
for val in history: | |
if val[0]: | |
messages.append({"role": "user", "content": val[0]}) | |
if val[1]: | |
messages.append({"role": "assistant", "content": val[1]}) | |
messages.append({"role": "user", "content": message}) | |
# Prepare input prompt for generation | |
prompt = "\n".join([f"{m['role']}: {m['content']}" for m in messages]) | |
inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
# Generate response | |
outputs = model.generate( | |
**inputs, | |
max_length=max_tokens, | |
temperature=temperature, | |
top_p=top_p, | |
pad_token_id=tokenizer.eos_token_id, | |
eos_token_id=tokenizer.eos_token_id | |
) | |
response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
response = response.split("assistant:")[-1].strip() # Clean response | |
return response | |
# -------------------- | |
# Gradio Interface | |
# -------------------- | |
demo = gr.ChatInterface( | |
respond, | |
additional_inputs=[ | |
gr.Textbox(value="You are a helpful assistant.", label="System message"), | |
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
], | |
) | |
# -------------------- | |
# Launch the Interface | |
# -------------------- | |
if __name__ == "__main__": | |
demo.launch() | |