import spaces from transformers import AutoTokenizer, AutoModelForCausalLM import torch import os import gradio as gr import sentencepiece os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:120' model_id = "thesven/Llama3-8B-SFT-code_bagel-bnb-4bit" tokenizer_path = "./" DESCRIPTION = """ # thesven/Llama3-8B-SFT-code_bagel-bnb-4bit """ tokenizer = AutoTokenizer.from_pretrained(model_id, device_map="auto", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", torch_dtype=torch.bfloat16, trust_remote_code=True) def format_prompt(user_message, system_message="You are an expert developer in all programming languages. Help me with my code. Answer any questions I have with code examples."): prompt = f"<|im_start|>assistant\n{system_message}<|im_end|>\n<|im_start|>\nuser\n{user_message}<|im_end|>\nassistant\n" return prompt @spaces.GPU def predict(message, system_message, max_new_tokens=600, temperature=3.5, top_p=0.9, top_k=40, do_sample=False): formatted_prompt = format_prompt(message, system_message) input_ids = tokenizer.encode(formatted_prompt, return_tensors='pt') input_ids = input_ids.to(model.device) response_ids = model.generate( input_ids, max_length=max_new_tokens + input_ids.shape[1], temperature=temperature, top_p=top_p, top_k=top_k, no_repeat_ngram_size=9, pad_token_id=tokenizer.eos_token_id, do_sample=do_sample ) response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True) truncate_str = "<|im_end|>" if truncate_str and truncate_str in response: response = response.split(truncate_str)[0] return [("bot", response)] with gr.Blocks() as demo: gr.Markdown(DESCRIPTION) with gr.Group(): system_prompt = gr.Textbox(placeholder='Provide a System Prompt In The First Person', label='System Prompt', lines=2, value="You are an expert developer in all programming languages. Help me with my code. Answer any questions I have with code examples.") with gr.Group(): chatbot = gr.Chatbot(label='thesven/Llama3-8B-SFT-code_bagel-bnb-4bit') with gr.Group(): textbox = gr.Textbox(placeholder='Your Message Here', label='Your Message', lines=2) submit_button = gr.Button('Submit', variant='primary') with gr.Accordion(label='Advanced options', open=False): max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=55000, step=1, value=512) temperature = gr.Slider(label='Temperature', minimum=0.1, maximum=4.0, step=0.1, value=0.1) top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9) top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=40) do_sample_checkbox = gr.Checkbox(label='Disable for faster inference', value=True) submit_button.click( fn=predict, inputs=[textbox, system_prompt, max_new_tokens, temperature, top_p, top_k, do_sample_checkbox], outputs=chatbot ) demo.launch()