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
---


# Nemotron-4-Minitron-4B-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

Support for Nemotron models will be added in the upcoming transformers library release. In the meantime, please install the library from source:

```

pip install git+https://github.com/huggingface/transformers

```

The following code provides an example of how to load the Minitron-4B model and use it to perform text generation.

```python

import torch

from transformers import AutoTokenizer, AutoModelForCausalLM



# Load the tokenizer and model

model_path = 'nvidia/Nemotron-4-Minitron-4B-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 = 'Complete the paragraph: our solar system is'

inputs = tokenizer.encode(prompt, return_tensors='pt').to(model.device)



# Generate the output

outputs = model.generate(inputs, max_length=20)



# Decode and print the output

output_text = tokenizer.decode(outputs[0])

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).

## Evaluation Results

*5-shot performance.* Language Understanding evaluated using [Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300):

| Average |
| :---- |
| 58.6 |

*Zero-shot performance.* Evaluated using select datasets from the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) with additions:

| HellaSwag | Winogrande | GSM8K| ARC-C | XLSum |
| :------------- | :------------- | :------------- | :------------- | :------------- |
| 75.0 | 74.0 | 24.1  | 50.9 | 29.5


*Code generation performance*. Evaluated using [HumanEval](https://github.com/openai/human-eval):

| p@1, 0-Shot |
| :------------- |
| 23.3 |

Please refer to our [paper](https://arxiv.org/abs/2407.14679) for the full set of results.

## 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}, 

}

```