Llama-3.1-Minitron-4B-Width-Base
Maintainer: nvidia - Last updated 9/18/2024
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 |
📈
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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Minitron-8B-Base is a large language model (LLM) obtained by pruning Nemotron-4 15B; specifically, the model embedding size, number of attention heads, and MLP intermediate dimension are pruned. Following pruning, continued training with distillation is performed using 94 billion tokens to arrive at the final model. The training corpus used is the same continuous pre-training data corpus used for Nemotron-4 15B. Deriving the Minitron 8B and 4B models from the base 15B model using this approach requires up to 40x fewer training tokens per model compared to training from scratch, resulting 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. Model Inputs and Outputs Inputs Text**: The model takes text input as a string. Outputs Text**: The model generates text output as a string. Capabilities Minitron-8B-Base can be used for a variety of natural language processing tasks such as text generation, summarization, and language understanding. The model's performance is comparable to other large language models, with the added benefit of reduced training costs due to the pruning and distillation approach used to create it. What Can I Use It For? Minitron-8B-Base can be used for research and development purposes, such as building prototypes or exploring novel applications of large language models. The model's efficient training process makes it an attractive option for organizations looking to experiment with LLMs without the high computational costs associated with training from scratch. Things to Try One interesting aspect of Minitron-8B-Base is its ability to perform well on various benchmarks while requiring significantly fewer training resources compared to training a model from scratch. Developers could explore ways to further fine-tune or adapt the model for specific use cases, leveraging the model's strong starting point to save time and computational resources.
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