Nemotron-4-Minitron-4B-Base
Maintainer: nvidia
117
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
Nemotron-4-Minitron-4B-Base is a large language model (LLM) obtained by pruning the larger 15B parameter Nemotron-4 model. Specifically, the model size was reduced by pruning the embedding size, number of attention heads, and MLP intermediate dimension. Following pruning, the model was further trained using 94 billion tokens of the same pre-training data used for the original Nemotron-4 15B model.
Deriving the Minitron 8B and 4B models from the base 15B model in this way requires up to 40x fewer training tokens compared to training from scratch. This results in a 1.8x compute cost savings for training the full model family. The Minitron models also exhibit up to a 16% improvement in MMLU scores compared to training from scratch, and perform comparably to other community models like Mistral 7B, Gemma 7B and Llama-3 8B, while outperforming state-of-the-art compression techniques.
Model Inputs and Outputs
Inputs
- Text: The model takes text input in the form of a string.
Outputs
- Text: The model generates text output in the form of a string.
Capabilities
Nemotron-4-Minitron-4B-Base is a large language model capable of tasks like text generation, summarization, and question answering. It can be used to generate coherent and contextually relevant text, and has shown strong performance on language understanding benchmarks like MMLU.
What Can I Use It For?
The Nemotron-4-Minitron-4B-Base model can be used as a foundation for building custom language models and applications. For example, you could fine-tune the model on domain-specific data to create a specialized assistant for your business, or use it to generate synthetic training data for other machine learning models.
The model is released under the NVIDIA Open Model License Agreement, which allows you to freely create and distribute derivative models.
Things to Try
One interesting aspect of the Nemotron-4-Minitron-4B-Base model is the approach used to derive the smaller Minitron variants. By pruning and further training the original Nemotron-4 15B model, the researchers were able to achieve significant compute cost savings while maintaining strong performance. You could experiment with different pruning and fine-tuning strategies to see if you can further optimize the model for your specific use case.
Another interesting area to explore would be the model's capability for few-shot and zero-shot learning. The paper mentions that the Minitron models perform comparably to other community models on various benchmarks, which suggests they may be able to adapt to new tasks with limited training data.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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