Spaces:
Sleeping
Sleeping
File size: 3,885 Bytes
433e378 |
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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the model and tokenizer
model_name = "Lyte/Llama-3.2-3B-Overthinker"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
def generate_response_stream(prompt, max_tokens, temperature, top_p, repeat_penalty, num_steps=4):
messages = [{"role": "user", "content": prompt}]
# Generate reasoning
reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
reasoning_ids = model.generate(
**reasoning_inputs,
max_new_tokens=max_tokens // 3,
temperature=temperature,
top_p=top_p,
repetition_penalty=repeat_penalty
)
reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
yield reasoning_output, "", ""
# Generate thinking (step-by-step and verifications)
messages.append({"role": "reasoning", "content": reasoning_output})
thinking_template = tokenizer.apply_chat_template(messages, tokenize=False, add_thinking_prompt=True, num_steps=num_steps)
thinking_inputs = tokenizer(thinking_template, return_tensors="pt").to(model.device)
thinking_ids = model.generate(
**thinking_inputs,
max_new_tokens=max_tokens // 3,
temperature=temperature,
top_p=top_p,
repetition_penalty=repeat_penalty
)
thinking_output = tokenizer.decode(thinking_ids[0, thinking_inputs.input_ids.shape[1]:], skip_special_tokens=True)
yield reasoning_output, thinking_output, ""
# Generate final answer
messages.append({"role": "thinking", "content": thinking_output})
answer_template = tokenizer.apply_chat_template(messages, tokenize=False, add_answer_prompt=True)
answer_inputs = tokenizer(answer_template, return_tensors="pt").to(model.device)
answer_ids = model.generate(
**answer_inputs,
max_new_tokens=max_tokens // 3,
temperature=temperature,
top_p=top_p,
repetition_penalty=repeat_penalty
)
answer_output = tokenizer.decode(answer_ids[0, answer_inputs.input_ids.shape[1]:], skip_special_tokens=True)
yield reasoning_output, thinking_output, answer_output
with gr.Blocks() as iface:
gr.Markdown("# Llama-3.2-3B Overthinker Customizable Steps, Please Duplicate and run with GPU if you can! T4 is fine!")
gr.Markdown("Generate responses using the Llama-3.2-3B Reasoning model.")
with gr.Row():
with gr.Column(scale=2):
prompt = gr.Textbox(lines=5, label="Prompt")
generate_button = gr.Button("Generate Response")
with gr.Column(scale=1):
max_tokens = gr.Slider(minimum=512, maximum=32768, value=8192, label="Max Number of Tokens")
temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.8, label="Temperature")
top_p = gr.Slider(minimum=0.01, maximum=0.99, value=0.95, label="Top P")
repeat_penalty = gr.Slider(minimum=0.5, maximum=2, value=1.1, label="Repeat Penalty")
num_steps = gr.Slider(minimum=1, maximum=10, value=4, label="Max Number of Steps")
reasoning_output = gr.Textbox(lines=5, label="Reasoning")
with gr.Accordion("Thinking Process", open=False):
thinking_output = gr.Textbox(lines=10, label="Step-by-Step Thinking")
answer_output = gr.Textbox(lines=5, label="Final Answer")
generate_button.click(
fn=generate_response_stream,
inputs=[prompt, max_tokens, temperature, top_p, repeat_penalty, num_steps],
outputs=[reasoning_output, thinking_output, answer_output]
)
iface.launch() |