Llama-3-8B-16K
| Property | Value |
|---|---|
| Base Model | LLaMA 3 8B |
| Context Length | 16K tokens |
| Training Hardware | 8x A6000 GPUs |
| Training Duration | 5 hours |
| Model URL | Hugging Face |
What is Llama-3-8B-16K?
Llama-3-8B-16K is an enhanced version of the LLaMA 3 8B base model, specifically optimized for processing longer text sequences up to 16,000 tokens. Developed by mattshumer, this model represents a significant advancement in handling extended context windows while maintaining the core capabilities of the original LLaMA 3 architecture.
Implementation Details
The model was trained using the Axolotl framework on the Yukang/LongAlpaca-16k-length dataset. A notable technical modification includes setting the rope_theta parameter to 1000000.0, which helps in better handling of longer sequences. The training process was conducted on 8 A6000 GPUs over a five-hour period, focusing on optimizing the model's ability to process and understand extended contexts.
- Extended context window of 16K tokens
- Modified rope_theta parameter for improved long-sequence handling
- Trained on LongAlpaca dataset
- Utilizes Axolotl training framework
Core Capabilities
- Processing and understanding of long-form text up to 16K tokens
- Maintains base LLaMA 3 capabilities while extending context window
- Suitable for tasks requiring extended context understanding
- Optimized for memory efficiency with 8B parameters
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its extended context window of 16K tokens while maintaining the efficiency of the 8B parameter base model. The modified rope_theta parameter and specific training on long-context datasets make it particularly suitable for tasks requiring understanding of longer text sequences.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring processing of long documents, extended conversations, or analysis of large text chunks. This could include document summarization, long-form content generation, and complex analytical tasks requiring extended context understanding.
