README.md

---
license: apache-2.0
base_model:
- meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
---
# Thought-Ranked Llama 3.2 3B v2

## What's New in v2?

The biggest improvement in v2 is how the model thinks through problems. Instead of just one level of thoughts, it can now explore up to 6 levels deep, building on its best ideas at each step. Think of it like having a conversation with yourself, where each new thought builds on your previous best insight.

## How It Works

Let's look at an example. When asked "What would happen if the moon disappeared?", the model might think:

```
<thoughts>
<thought>First, I should consider the moon's main effects on Earth</thought>
<thought>The moon controls our tides, so ocean patterns would change dramatically</thought>
<thought>Without the moon's gravitational pull, Earth's rotation would become unstable</thought>
<thought>This would lead to extreme climate changes and disrupted ecosystems</thought>
<thought>The loss of moonlight would affect nocturnal animals and human culture</thought>
<thought>Combining all these effects, we'd see a cascade of environmental changes</thought>
</thoughts>

The disappearance of the moon would have far-reaching consequences for Earth...
[detailed answer follows]
```

### System Messages

The model responds to different types of system prompts. Here are some examples:

1. Basic prompt:
```
{"role": "system", "content": "You are a helpful assistant. Think before responding."}
```

2. Specific thought count:
```
{"role": "system", "content": "You are a helpful assistant. Think 3 thoughts before responding."}
```

3. Standard helper:
```
{"role": "system", "content": "You are a helpful assistant."}
```

About 40% of training examples include system messages, and 75% of those specifically mention thinking. This helps the model learn when and how much to think through problems.

## Technical Details

- **Base Model**: Llama 3.2 3B
- **Training Data**: 2,500 carefully selected examples, each with up to 6 levels of thought chains
- **Thought Selection**: At each level, the model generates multiple possible thoughts and picks the best one using an external ranking system

## What's It Good For?

This model excels at tasks that benefit from careful thinking:

✅ Breaking down complex problems
✅ Step-by-step math solutions
✅ Detailed analysis of situations
✅ Explaining complicated concepts
✅ Making well-reasoned decisions

## Limitations

- Can sometimes overthink simple problems
- Limited by the capabilities of the base Llama 3.2 3B model
- Not suitable for critical decisions without human oversight
- May occasionally generate irrelevant thought chains

## Example Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ericflo/Llama-3.2-3B-COT-v2")
tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COT-v2")

messages = [
    {"role": "system", "content": "You are a helpful assistant. Think 3 thoughts before responding."},
    {"role": "user", "content": "How would you teach a child to ride a bike?"}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
output = model.generate(input_ids, temperature=1.0)
response = tokenizer.decode(output[0])
```

Example output:
```
<thoughts>
<thought>Safety should be the first priority - helmet and protective gear</thought>
<thought>Starting with balance using training wheels can build confidence</thought>
<thought>Breaking the process into small, manageable steps will help avoid overwhelm</thought>
</thoughts>

Here's how I would teach a child to ride a bike...
[detailed answer follows]
```

## Citation

```bibtex
@misc{thought-ranked-llama-v2,
  title={Thought-Ranked Llama 3.2 v2: Hierarchical Chain-of-Thought Generation},
  author={[Eric Florenzano]},
  year={2024},
  howpublished={\url{https://huggingface.co/ericflo/Llama-3.2-3B-COT-v2}}
}
```

## Acknowledgments

This model builds on the Llama 3.2 3B base model from Meta. Special thanks to the open-source AI community for their contributions to chain-of-thought prompting techniques.