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by abacusai · Agent Tool · ★ 605
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Extending LLM Context Length The choice of how to encode positional information for transformers has been one of the key components of LLM architectures. An area that has been interesting to us and others in the community recently is whether LLMs can be extended to longer contexts. We have conducted a range of experiments with different schemes for extending context length capabilities of Llama, which has been pretrained on 2048 context length with the RoPE (Rotary Position Embedding) encoding. Here we share some of the results as well as the training and evaluation scripts in the hope that it will be useful to the community. For our best performing models - linear scaling with IFT at scales 4 and 16 - we are also sharing the weights in case others wish to use them, or to conduct their own tests. We believe the scale 16 model should perform well on real world tasks up to 16k context lengths, and potentially even up to about 20-24k context lengths. Scale 16 model Technical Paper Overview We conducted a wide variety of experiments to try to extend the context length of the models. First, we tried simply using the base Llama model zero-shot.
| Stars | 605 |
| Forks | 47 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 52.9599012604374/100 |
| Open Issues | 7 |
| Last Updated | 2023-11-17 |
| Created | 2023-07-27 |
| Platforms | python |
| Est. Tokens | ~36k |
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Long-Context is This repository contains code and tooling for the Abacus.AI LLM Context Expansion project. Also included are evaluation scripts and benchmark tasks that evaluate a model’s information retrieval capabi. It is categorized as a Agent Tool with 605 GitHub stars.
Long-Context is primarily written in Python.
You can find installation instructions and usage details in the Long-Context GitHub repository at github.com/abacusai/Long-Context. The project has 605 stars and 47 forks, indicating an active community.
Long-Context is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
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