README.md

---
license: apache-2.0
base_model:
- meta-llama/Llama-3.2-3B
tags:
- llama-3.2
- thought-chain
- instruction-finetuning
- transformers
library_name: transformers
pipeline_tag: text-generation
---

# Thought-Ranked Llama 3.2 3B

## Model Description

This model is a fine-tuned version of Meta's Llama 3.2 3B (Base) that has been specially trained to generate high-quality thought processes before producing answers. The model underwent 4 rounds of specialized fine-tuning using a thought-chain ranking approach.
(Weekend project, just a few hundred steps of training)

### Training Process

1. **Initial Generation**: For each training sample, the model generates multiple thought chains by prefixing different thought tokens: `<thought>{char}</thought>` for each character in `[a-zA-Z0-9]`. Each thought chain is allowed up to 128 tokens.

2. **Answer Generation**: Following each thought chain, the model generates a complete answer with up to 2048 tokens.

3. **Ranking & Selection**: An external LLM ranking system evaluates the quality of answers without seeing the thought processes, creating a ranking of the most effective thought patterns.

4. **Final Training**: The model is then trained on the highest-ranked thought-answer pairs, learning to generate the most effective thought patterns autonomously.

### Key Features

- **Thought Chain Generation**: The model has learned to generate explicit thought processes before providing answers
- **Greedy Sampling**: Uses greedy sampling for both thought generation and final answers
- **Length Parameters**:
  - Thought chains: Up to 128 tokens
  - Final answers: Up to 2048 tokens

### Model Architecture

- Base model: Llama 3.2 3B (Base)
- Architecture: Transformer-based language model
- Parameters: ~3.2 billion
- Training Strategy: Supervised Fine-Tuning (SFT) with thought-chain ranking

## Intended Use

This model is designed for tasks that benefit from explicit reasoning chains, including but not limited to:
- Problem-solving
- Mathematical reasoning
- Logical deduction
- Step-by-step explanations
- Complex decision making

### Out-of-Scope Uses

- Direct deployment without safety measures
- Applications requiring guaranteed accuracy
- Critical decision-making without human oversight
- Tasks requiring capabilities beyond the base Llama 3.2 3B model

## Training Details

### Training Data

The model was trained using:
- Sample questions paired with multiple thought variations
- Thought chains generated using systematic character prefixes
- Rankings derived from LLM evaluation of answer quality

### Training Procedure

1. **Thought Generation Phase**
   - Generated 62 variations of thoughts per sample (a-z, A-Z, 0-9)
   - Sampled with temperature=0.0
   - Maximum thought length: 128 tokens

2. **Answer Generation Phase**
   - Generated completions following each thought chain
   - Maximum answer length: 2048 tokens
   - Sampled with temperature=0.0

3. **Ranking Phase**
   - External LLM evaluated answer quality
   - Ranking performed without access to thought chains
   - Selected highest-performing thought-answer pairs

4. **Final Training Phase**
   - Fine-tuned on best-performing thought-answer combinations
   - 4 complete rounds of training

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ericflo/Llama-3.2-3B-COT")
tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COT")

# Example usage
prompt = "Solve this math problem: 2x + 3 = 7"
input_ids = tokenizer.apply_chat_template(
  [{"role": "user", "content": prompt}],
  return_tensors="pt"
)

# Generate response with thought chain
output = model.generate(
    input_ids,
    temperature=1.0,
)

response = tokenizer.decode(output[0])
```

## Limitations

- Limited to the capabilities of the base Llama 3.2 3B model
- May generate thought chains that are not always optimal
- Performance depends on the quality of the LLM ranking system used during training
- Training process may not capture all possible effective thought patterns
- Limited by the context window of the base model

## Ethical Considerations

- The model inherits biases from the base Llama 3.2 3B model
- Generated thought chains should be reviewed for accuracy and appropriateness
- The model's reasoning process should not be relied upon for critical decisions without human verification
- Users should implement appropriate content filtering and safety measures

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{thought-ranked-llama,
  title={Thought-Ranked Llama 3.2: Fine-tuning Language Models with Ranked Thought Chains},
  author={[Eric Florenzano]},
  year={2024},
  howpublished={\url{https://huggingface.co/ericflo/Llama-3.2-3B-COT}}
}
```