Spaces:
Runtime error
Runtime error
File size: 1,099 Bytes
e5ff2a5 018dc64 e5ff2a5 2dfc7b0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 |
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the model and tokenizer
model_name = "mattshumer/Reflection-Llama-3.1-70B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
def generate_response(message, history):
# Combine history and new message
prompt = "\n".join([f"Human: {h[0]}\nAI: {h[1]}" for h in history])
prompt += f"\nHuman: {message}\nAI:"
# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=500, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the AI's response
ai_response = response.split("AI:")[-1].strip()
return ai_response
# Create the Gradio interface
iface = gr.ChatInterface(
fn=generate_response,
title="Chat with Reflection-Llama-3.1-70B",
description="Ask me anything!",
)
# Launch the interface
iface.launch() |