71 lines
2.3 KiB
Markdown
71 lines
2.3 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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---
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# FILM-7B
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<p align="center">
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💻 <a href="https://github.com/microsoft/FILM/" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2404.16811" target="_blank">[Paper]</a> • ⚓ <a href="https://huggingface.co/datasets/In2Training/VaLProbing-32K" target="_blank">[VaLProbing-32K] </a>
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</p>
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**FILM-7B is a 32K-context LLM that overcomes the lost-in-the-middle problem.**
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It is trained from Mistral-7B-Instruct-v0.2 by applying Information-Intensie (In2) Training.
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FILM-7B achieves near-perfect performance on probing tasks, SOTA-level performance on real-world long-context tasks among ~7B size LLMs, and does not compromise the short-context performance.
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## Model Usage
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The system tempelate for FILM-7B:
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```text
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'''[INST] Below is a context and an instruction. Based on the information provided in the context, write a response for the instruction.
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### Context:
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{YOUR LONG CONTEXT}
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### Instruction:
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{YOUR QUESTION & INSTRUCTION} [/INST]
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'''
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```
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## Probing Results
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To reproduce the results on our VaL Probing, see the guidance in [https://github.com/microsoft/FILM/tree/main/VaLProbing](https://github.com/microsoft/FILM/tree/main/VaLProbing).
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<p align="center">
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<img src="./figures/probing_results_new.png" width="800">
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<br>
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</p>
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## Real-World Long-Context Tasks
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To reproduce the results on real-world long-context tasks, see the guidance in [https://github.com/microsoft/FILM/tree/main/real_world_long](https://github.com/microsoft/FILM/tree/main/real_world_long).
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<p align="center">
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<img src="./figures/real_world_long.png" width="800">
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<br>
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</p>
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## Short-Context Tasks
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To reproduce the results on short-context tasks, see the guidance in [https://github.com/microsoft/FILM/tree/main/short_tasks](https://github.com/microsoft/FILM/tree/main/short_tasks).
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<p align="center">
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<img src="./figures/short.png" width="800">
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<br>
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</p>
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## 📝 Citation
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```
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@misc{an2024make,
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title={Make Your LLM Fully Utilize the Context},
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author={Shengnan An and Zexiong Ma and Zeqi Lin and Nanning Zheng and Jian-Guang Lou},
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year={2024},
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eprint={2404.16811},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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Disclaimer: This model is strictly for research purposes, and not an official product or service from Microsoft.
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