Llama-3-8B-Instruct-Gradient-4194k

Maintainer: gradientai

Total Score

52

Last updated 5/19/2024

👨‍🏫

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The Llama-3-8B-Instruct-Gradient-4194k model is an extension of the Meta Llama 3 8B instruction-tuned model, developed by Gradient. This model increases the context length from 8k to 4194k tokens, demonstrating that large language models can learn to operate on long context with minimal training by appropriately adjusting the Rotation Position Encoding (RoPE) parameter.

Similar models include the Llama-3-70B-Instruct-Gradient-1048k and Llama-3-8B-Instruct-262k models, which also extend the context length of Llama 3 using progressive training and RoPE optimization techniques.

Model inputs and outputs

Inputs

  • The model takes in text input only.

Outputs

  • The model generates text and code.

Capabilities

The Llama-3-8B-Instruct-Gradient-4194k model demonstrates strong performance on a range of benchmarks, especially for tasks that require reasoning over long contexts. By extending the context length, the model is able to maintain high performance on tasks like TriviaQA-Wiki and DROP, where longer-range understanding is important.

What can I use it for?

This model could be useful for a variety of applications that benefit from long-context understanding, such as question answering, task-oriented dialogues, and code generation. Developers can leverage this model as a starting point to build custom AI agents and assistants that power critical operations across their business. Those interested in working with Gradient to develop custom LLMs and AI systems can reach out at contact@gradient.ai.

Things to try

One interesting aspect of this model is its demonstrated ability to learn to operate on long contexts with minimal training data - just 201M tokens for this stage, and 1.6B total across all stages. This suggests the potential to adapt large language models to new domains and tasks efficiently, without requiring enormous datasets. Developers could experiment with fine-tuning this model on their own data to adapt it to specific use cases.



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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