Primary uses:
- Research into model merging and alignment
- Experimentation with LLM capabilities
- Educational exploration of LLM behavior
Out-of-scope uses:
- Deployment in high-stakes domains (medical, legal, financial, etc.)
- Applications requiring factual reliability without human oversight
- Safety-critical systems

How to Use LlamaHarmony

Getting started is straightforward with Hugging Face's ecosystem:

Installation:

!pip install transformers
Loading the Model:

python
1from transformers import AutoModelForCausalLM, AutoTokenizer 2 3model_name = "AIGym/LlamaHarmony" 4tokenizer = AutoTokenizer.from_pretrained(model_name) 5model = AutoModelForCausalLM.from_pretrained(model_name)
Generating Text:

python
1input_text = "Once upon a time in a harmonious AI world..." 2inputs = tokenizer(input_text, return_tensors="pt") 3outputs = model.generate(**inputs, max_length=100) 4print(tokenizer.decode(outputs[0]))

For advanced usage, experiment with fine-tuning on your datasets or integrating into pipelines for chatbots/research tools. If you need inference support (e.g., via APIs), request it through the model's discussion page on Hugging Face.

Training Data

The model is a merge of existing LLMs.
No new training data was collected.
Underlying datasets come from the component base models.

Evaluation

Evaluation methods: TBD (to be added once benchmark results are available).
Known performance characteristics: Not yet comprehensively evaluated.
Limitations: May inherit weaknesses and biases from source models.

Ethical Considerations & Risks

May produce biased, offensive, or hallucinated outputs.
Should not be relied on for factual correctness.
Use in decision-making contexts must include human oversight.

Environmental Impact

As a merged model, the environmental cost of training was limited compared to training from scratch.
Further details depend on the base models used.

License

License: Same as the base models (check compatibility before redistribution).

Citation

If you use this model, please cite:

@misc{loring2025testmerge,
  title={AIGym/LlamaHarmony: A Merged Llama Large Language Model with the harmony tokenizer},
  author={Loring, Dustin},
  year={2025},
  howpublished={\url{https://huggingface.co/AIGym/LlamaHarmony}}
}