README.md

---

license: other
license_name: nvidia-open-model-license
license_link: >-
  https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
---


# Minitron 8B Base

Minitron is a family of small language models (SLMs) obtained by pruning NVIDIA's [Nemotron-4 15B](https://arxiv.org/abs/2402.16819) model. We prune model embedding size, attention heads, and MLP intermediate dimension, following which, we perform continued training with distillation to arrive at the final models.

Deriving the Minitron 8B and 4B models from the base 15B model using our approach requires up to **40x fewer training tokens** per model compared to training from scratch; this results in **compute cost savings of 1.8x** for training the full model family (15B, 8B, and 4B). Minitron models exhibit up to a 16% improvement in MMLU scores compared to training from scratch, perform comparably to other community models such as Mistral 7B, Gemma 7B and Llama-3 8B, and outperform state-of-the-art compression techniques from the literature. Please refer to our [arXiv paper](https://arxiv.org/abs/2407.14679) for more details.

Minitron models are for research and development only.

## HuggingFace Quickstart

The [PR](https://github.com/huggingface/transformers/pull/31699) to support our models in Hugging Face in under review and expected to be merged soon. In the meantime, this [branch](https://github.com/suiyoubi/transformers/tree/aot/nemotron-support) at [commit ID 63d9cb0](https://github.com/suiyoubi/transformers/commit/63d9cb0afd2bf5d4cb5431ba1b2c4e353752a937) can be used for Minitron models:

```

git clone git@github.com:suiyoubi/transformers.git

cd transformers

git checkout 63d9cb0

pip install .

```
The following code provides an example of how to load the Minitron-8B model and use it to perform text generation.

```python

import torch

from transformers import AutoTokenizer, AutoModelForCausalLM



# Load the tokenizer and model

model_path = "nvidia/Minitron-8B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_path)



device='cuda'

dtype=torch.bfloat16

model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)



# Prepare the input text

prompt = "To be or not to be,"

input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)



# Generate the output

output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1)



# Decode and print the output

output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print(output_text)

```

## License

Minitron is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).

## Citation

If you find our work helpful, please consider citing our paper:
```

@article{minitron2024,

      title={Compact Language Models via Pruning and Knowledge Distillation}, 

      author={Saurav Muralidharan and Sharath Turuvekere Sreenivas and Raviraj Joshi and Marcin Chochowski and Mostofa Patwary and Mohammad Shoeybi and Bryan Catanzaro and Jan Kautz and Pavlo Molchanov},

      journal={arXiv preprint arXiv:2407.14679},

      year={2024},

      url={https://arxiv.org/abs/2407.14679}, 

}

```