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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import AutoModel, AutoTokenizer
from adapters import AutoAdapterModel
model_name = "unsloth/Meta-Llama-3.1-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Load the base model with adapters
model = AutoAdapterModel.from_pretrained(model_name)
model.load_adapter("Braszczynski/Llama-3.2-3B-Instruct-bnb-4bit-460steps")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Combine system message and chat history
chat_history = f"{system_message}\n"
for user_msg, bot_reply in history:
chat_history += f"User: {user_msg}\nAssistant: {bot_reply}\n"
chat_history += f"User: {message}\nAssistant:"
# Tokenize the input
inputs = tokenizer(chat_history, return_tensors="pt", truncation=True).to("cuda")
# Generate response
outputs = model.generate(
inputs["input_ids"],
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.eos_token_id
)
# Decode and format the output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
response = response[len(chat_history):].strip() # Remove input context from output
return response
# Define the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly 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)"),
],
)
if __name__ == "__main__":
demo.launch()
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