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--- |
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license: llama3 |
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datasets: |
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- princeton-nlp/prolong-data-64K |
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- princeton-nlp/prolong-data-512K |
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base_model: |
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- princeton-nlp/Llama-3-8B-ProLong-64k-Base |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/Llama-3-8B-ProLong-512k-Base-GGUF |
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This is quantized version of [princeton-nlp/Llama-3-8B-ProLong-512k-Base](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Base) created using llama.cpp |
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# Original Model Card |
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# princeton_nlp/Llama-3-8B-ProLong-512k-Base |
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[[Paper](https://arxiv.org/pdf/2410.02660)] [[HF Collection](https://huggingface.co/collections/princeton-nlp/prolong-66c72d55d2051a86ac7bd7e4)] [[Code](https://github.com/princeton-nlp/ProLong)] |
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**ProLong** (<u>Pr</u>incet<u>o</u>n <u>long</u>-context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our [main ProLong model](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct) is one of the best-performing long-context models at the 10B scale (evaluated by [HELMET](https://github.com/princeton-nlp/helmet)). |
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To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data, SFT data, and numerous other design choices. We demonstrate our findings in our paper, [How to Train Long-Context Language Models (Effectively)](https://arxiv.org/pdf/2410.02660). |
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Authors: [Tianyu Gao](https://gaotianyu.xyz/about)\*, [Alexander Wettig](https://www.cs.princeton.edu/~awettig/)\*, [Howard Yen](https://howard-yen.github.io/), [Danqi Chen](https://www.cs.princeton.edu/~danqic/) (* equal contribution) |
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Contact: `{tianyug, awettig}@princeton.edu` |
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## The ProLong Models |
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- [princeton_nlp/Llama-3-8B-ProLong-64k-Base](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-64k-Base) |
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- [princeton_nlp/Llama-3-8B-ProLong-64k-Instruct](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-64k-Instruct) |
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- [princeton_nlp/Llama-3-8B-ProLong-512k-Base](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Base) ← you are here! |
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- ⭐ [princeton_nlp/Llama-3-8B-ProLong-512k-Instruct](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct) |
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## Model card |
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Here are some quick facts about our main ProLong model: [princeton-nlp/Llama-3-8B-ProLong-512k-Instruct](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct). |
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* Base model: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) |
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* Long-context continued training: 20B tokens on 64K training data ([princeton-nlp/prolong-data-64K](https://huggingface.co/datasets/princeton-nlp/prolong-data-64K)), and 20B tokens on 512K training data ([princeton-nlp/prolong-data-512K](https://huggingface.co/datasets/princeton-nlp/prolong-data-512K)) |
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* Supervised fine-tuning (SFT): [UltraChat](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) |
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* Maximum context window: 512K tokens |
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<p align="center" style="margin-bottom: 0;"> |
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<img width="80%" alt="image" src="https://github.com/user-attachments/assets/c31c9671-49fe-4776-91d2-de70ffd9f9a1"> |
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</p> |
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<p align="center" style="margin-top: 0; padding-top: 0;"> |
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<em>ProLong performance on <a href="https://github.com/princeton-nlp/helmet">HELMET</a> averaged over 32K, 64K, and 128K lengths. All models are instruct models.</em> |
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</p> |
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<p align="center"> |
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<img width="80%" alt="image" src="https://github.com/user-attachments/assets/a36a7d0f-4480-4a29-80f3-208477707fb7"> |
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</p> |
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<p align="center" style="margin-top: 0;"> |
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<em>ProLong training recipe.</em> |
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</p> |
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## Citation |
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```bibtex |
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@article{gao2024prolong, |
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title={Enabling Large Language Models to Generate Text with Citations}, |
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author={Gao, Tianyu and Wettig, Alexander and Yen, Howard and Chen, Danqi}, |
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year={2024}, |
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} |
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``` |
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