README.md

---
license: apache-2.0
language:
- en
- zh
base_model:
- meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
tags:
- CoT
- LongCoT
- o1
pipeline_tag: text-generation
---

# Llama-3.2-3B-LongCoT

A small model with **LongCoT** capability.

![Example Image](images/example.jpg)

## Features

- Using high-quality synthetic data for fine-tuning.
- The model can adjust whether to use LongCoT based on the complexity of the question.
- Good at mathematics and reasoning

## Benchmark

| Benchmark | Llama-3.2-3B-Instruct | Llama-3.2-3B-LongCoT |
|-----------|-----------------------|----------------------|
| Math      | 35.5                 | **52.0**                 |
| GSM8K     | 77.3                  | **82.3**                 |

## Inference

Example of Stream Inference: 

```python
import time
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextStreamer,
)

# Model ID from Hugging Face
model_id = "Kadins/Llama-3.2-3B-LongCoT"

# Load the pre-trained model with appropriate data type and device mapping
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,  # Use bfloat16 for optimized performance
    device_map="auto",           # Automatically map the model to available devices
)

# Load the tokenizer associated with the model
tokenizer = AutoTokenizer.from_pretrained(model_id)

def stream_chat(messages, max_new_tokens=8192, top_p=0.95, temperature=0.6):
    """
    Generates a response using streaming inference.

    Args:
        messages (list): A list of dictionaries containing the conversation prompt.
        max_new_tokens (int): Maximum number of tokens to generate.
        top_p (float): Nucleus sampling parameter for controlling diversity.
        temperature (float): Sampling temperature to control response creativity.
    """
    # Prepare the input by applying the chat template and tokenizing
    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt",
        return_dict=True,  # Ensure the output is a dictionary
    ).to(model.device)  # Move the inputs to the same device as the model

    # Initialize the TextStreamer for real-time output
    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    # Record the start time for performance measurement
    start_time = time.time()

    # Generate the response using the model's generate method with streaming
    model.generate(
        **inputs,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        repetition_penalty=1.1,
        top_p=top_p,
        temperature=temperature,
        streamer=streamer,  # Enable streaming of the generated tokens
    )

    # Calculate and print the total response time
    total_time = time.time() - start_time
    print(f"\n--- Response finished in {total_time:.2f} seconds ---")

def chat_loop():
    """
    Initiates an interactive chat session with the model.
    Continuously reads user input and generates model responses until the user exits.
    """
    while True:
        # Initialize the conversation with a system message
        messages = [
            {"role": "system", "content": "You are a reasoning expert and helpful assistant."},
        ]

        # Prompt the user for input
        user_input = input("\nUser: ")
        if user_input.strip().lower() in ["exit", "quit"]:
            print("Exiting chat...")
            break

        # Append the user's message to the conversation history
        messages.append({"role": "user", "content": user_input})

        print("Assistant: ", end="", flush=True)

        # Generate and stream the assistant's response
        stream_chat(messages)

        # Note: Currently, the assistant's reply is streamed directly to the console.
        # To store the assistant's reply in the conversation history, additional handling is required.

if __name__ == "__main__":
    # Start the interactive chat loop when the script is executed
    chat_loop()
```