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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "LivSterling/rc-tutor-llama3-merged"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
device_map="auto",
dtype=torch.float16,
)
# LLaMA-3 padding fix
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = tokenizer.eos_token_id
def chat_fn(message, history):
messages = []
for user, bot in history:
messages.append({"role": "user", "content": user})
messages.append({"role": "assistant", "content": bot})
messages.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
).to(model.device)
attention_mask = input_ids != tokenizer.pad_token_id
with torch.no_grad():
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=256,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
return tokenizer.decode(
outputs[0][input_ids.shape[-1]:],
skip_special_tokens=True,
).strip()
demo = gr.ChatInterface(
fn=chat_fn,
title="RC Tutor LLaMA-3",
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
ssr_mode=False,
show_error=True,
)
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