Welcome to the world of SuperCorrect! In this blog post, we will walk you through implementing a novel two-stage fine-tuning method designed to enhance the reasoning accuracy and self-correction capabilities of Large Language Models (LLMs). Whether you’re a seasoned developer or a curious newcomer, this user-friendly guide will help you get started with this cutting-edge technology.

Understanding the Concept

Before we dive into the implementation, let’s clarify the analogy: imagine you are training a young student to solve math problems. Initially, they might solve problems using trial and error, which can lead to mistakes. However, by introducing a structured approach, like a step-by-step guide, they learn not only the correct answers but the reasoning behind them. SuperCorrect functions similarly by integrating a pre-defined hierarchical thought template called the Buffer of Thought (BoT) to guide LLMs through deliberate reasoning.

Quick Start

Requirements

• Ensure you have the transformers library version 4.37.0 or higher installed. This is essential since it includes the necessary code for Qwen2.5 models.
• Python installed on your machine.
• A compatible CUDA-enabled device for optimal performance.

Inference Setup

Follow the steps below to set up your inference environment easily:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "BitStarWalkin/SuperCorrect-7B"
device = "cuda"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype='auto',
    device_map='auto'
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Find the distance between the foci of the ellipse [9x^2 + frac{y^2}{29} = 99.]"
hierarchical_prompt = "Solve the following math problem in a step-by-step XML format. Each step should be enclosed within tags like .... " \
                      "For each step enclosed within the tags, determine if this step is challenging. If so, provide annotations enclosed within ...."

messages = [
    {"role": "system", "content": hierarchical_prompt},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors='pt').to(device)

generated_ids = model.generate(**model_inputs, max_new_tokens=1024)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(response)

Performance Evaluation

After implementing the SuperCorrect model, it’s time to evaluate its performance. The SuperCorrect-7B model has shown significant improvements on popular benchmarks such as GSM8K and MATH.

Troubleshooting Tips

• If your model does not load properly, double-check that you are using the correct version of the transformers library.
• Ensure that your CUDA device is set up correctly and is compatible with the installed packages.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
• If you encounter model performance issues, consider going through the input formatting to ascertain that it aligns with the expected schema.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Now that you have a foundational understanding and step-by-step implementation process for SuperCorrect, you can leverage this knowledge to explore further into the realm of language models. Happy coding!