Llama-3.1-Minitron-4B-Width-Base
Maintainer: nvidia
178
🛠️
| 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
Llama-3.1-Minitron-4B-Width-Base is a base text-to-text model developed by NVIDIA that can be adopted for a variety of natural language generation tasks. It is obtained by pruning the larger Llama-3.1-8B model, specifically reducing the model embedding size, number of attention heads, and MLP intermediate dimension. The pruned model is then further trained with distillation using 94 billion tokens from the continuous pre-training data corpus used for Nemotron-4 15B.
Similar NVIDIA models include the Minitron-8B-Base and Nemotron-4-Minitron-4B-Base, which are also derived from larger language models through pruning and knowledge distillation. These compact models exhibit performance comparable to other community models, while requiring significantly fewer training tokens and compute resources compared to training from scratch.
Model Inputs and Outputs
Inputs
- Text: The model takes text input in string format.
- Parameters: The model does not require any additional input parameters.
- Other Properties: The model performs best with input text less than 8,000 characters.
Outputs
- Text: The model generates text output in string format.
- Output Parameters: The output is a 1D sequence of text.
Capabilities
Llama-3.1-Minitron-4B-Width-Base is a powerful text generation model that can be used for a variety of natural language tasks. Its smaller size and reduced training requirements compared to the full Llama-3.1-8B model make it an attractive option for developers looking to deploy large language models in resource-constrained environments.
What Can I Use It For?
The Llama-3.1-Minitron-4B-Width-Base model can be used for a wide range of natural language generation tasks, such as chatbots, content generation, and language modeling. Its capabilities make it well-suited for commercial and research applications that require a balance of performance and efficiency.
Things to Try
One interesting aspect of the Llama-3.1-Minitron-4B-Width-Base model is its use of Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE), which can improve its inference scalability compared to standard transformer architectures. Developers may want to experiment with these architectural choices and their impact on the model's performance and capabilities.
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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