Add initial README

README.md

@@ -4,3 +4,63 @@ license_name: nvidia-open-model-license
 license_link: >-
   https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
 ---
+
+# 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]() 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. This [branch](https://github.com/suiyoubi/transformers/tree/aot/nemotron-support) can be used meanwhile to use our Minitron models.
+
+```
+git clone [email protected]:suiyoubi/transformers.git
+cd transformers
+git checkout aot/nemotron-support
+pip install .
+```
+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/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 = "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:XXX},
+      year={2024}
+}
+```