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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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from peft import PeftModel |
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from threading import Thread |
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import torch |
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print("Loading base model...") |
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") |
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model = AutoModelForCausalLM.from_pretrained( |
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"meta-llama/Llama-3.1-8B-Instruct", |
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device_map="auto", |
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torch_dtype=torch.float16, |
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load_in_8bit=True, |
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) |
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print("Loading your fine-tuned adapter...") |
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model = PeftModel.from_pretrained(model, "drumwell/autotrain-2duhi-5mmyz") |
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print("Model loaded!") |
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def respond( |
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message, |
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history: list[dict[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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hf_token: gr.OAuthToken, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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messages.extend(history) |
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messages.append({"role": "user", "content": message}) |
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(text, return_tensors="pt").to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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generation_kwargs = dict( |
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inputs, |
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streamer=streamer, |
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max_new_tokens=max_tokens, |
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temperature=temperature, |
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top_p=top_p, |
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do_sample=True, |
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repetition_penalty=1.1, |
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) |
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thread = Thread(target=model.generate, kwargs=generation_kwargs) |
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thread.start() |
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response = "" |
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for token in streamer: |
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response += token |
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yield response |
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chatbot = gr.ChatInterface( |
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respond, |
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type="messages", |
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additional_inputs=[ |
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gr.Textbox(value="You are a BMW E30 M3 and 320is technical expert assistant. Answer accurately based on factory specifications.", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.3, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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) |
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with gr.Blocks() as demo: |
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with gr.Sidebar(): |
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gr.LoginButton() |
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chatbot.render() |
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if __name__ == "__main__": |
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demo.launch() |
