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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()
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