README.md

---
license: llama3.2
datasets:
- tatsu-lab/alpaca
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
tags:
- diffusion
- text-generation-inference
---
# llama3-diffusion-exp

An experimental diffusion-based language model fine-tuned from Meta's Llama 3.2 3B base model.

## Overview

llama3-diffusion-exp explores the application of diffusion techniques to language generation, offering variable inference speeds and unique generation characteristics. This model represents an experimental approach to combining diffusion methodologies with transformer-based language modeling.

## Model Details

- **Base Model**: Meta Llama 3.2 3B
- **Architecture**: Transformer with diffusion-based generation
- **Parameters**: ~3 billion
- **Training**: Fine-tuned using diffusion techniques
- **Status**: Experimental research model

## Performance Characteristics

All benchmarks conducted on NVIDIA A100 GPU without optimizations.

### Speed Performance (NVIDIA A100 with optimizations)
- **Base Speed**: 30 tokens/second
- **Maximum Speed**: Up to 150 tokens/second (5x acceleration)
- **Speed Variability**: Inference speed can be adjusted based on quality requirements
- **Comparison**: Standard autoregressive generation achieves ~13 tokens/second on the same hardware
- **Speedup**: 2.3x faster at base speed, up to 11.5x faster at maximum speed vs. normal generation

### Generation Quality
- **Optimal Use**: Short, coherent sentences
- **Limitations**: 
  - Longer sequences may exhibit word repetition
  - Complex sentences might become jumbled
  - Quality degrades with increased generation length

## Usage Recommendations

### Best Practices
- Use for short-form text generation (1-2 sentences)
- Ideal for rapid prototyping and experimentation
- Consider for applications requiring high-speed inference
- Experiment with different speed settings to balance quality and performance

### Limitations to Consider
- Not suitable for long-form content generation
- May require post-processing for longer outputs
- Experimental nature means results may be unpredictable
- Quality-speed trade-offs require careful tuning

## Use Cases

- **Rapid Prototyping**: Quick text generation for testing and development
- **Real-time Applications**: Low-latency text generation needs
- **Research**: Studying diffusion approaches in language modeling
- **Creative Writing**: Short phrase or sentence generation
- **Chatbots**: Brief response generation

## Technical Notes

This model implements diffusion-based generation techniques adapted for language modeling, which differs from traditional autoregressive generation. The variable speed characteristics come from the diffusion process allowing for different numbers of denoising steps.

## Limitations and Warnings

⚠️ **Experimental Model**: This is a research prototype and should be used accordingly.

- Output quality varies significantly with generation length
- Speed improvements come with potential quality trade-offs
- Not recommended for production applications without thorough testing
- May produce unexpected or incoherent outputs for complex prompts

## Installation and Usage

```python
# Example usage (implementation-dependent)
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("llama3-diffusion-exp")
tokenizer = AutoTokenizer.from_pretrained("llama3-diffusion-exp")

# Generate with speed control
output = model.generate(
    input_ids,
    max_length=50,  # Keep short for best results
    speed_factor=2.0  # Adjust speed (hypothetical parameter)
)
```

## Contributing

This is an experimental model. Feedback, bug reports, and research contributions are welcome. Please document any unusual behaviors or interesting findings.

## License

Please refer to the original Llama 3.2 license terms and any additional restrictions that may apply to this fine-tuned variant.

## Citation

If you use this model in your research, please cite both the original Llama 3.2 paper and acknowledge this experimental work.

## Acknowledgments

Built upon Meta's Llama 3.2 3B model. This experimental work explores novel applications of diffusion techniques to language generation.

---

**Disclaimer**: This is an experimental model intended for research purposes. Results may vary and should be validated for any specific use case.