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license: apache-2.0
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---
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license: apache-2.0
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datasets:
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- In2Training/VaLProbing-32K
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language:
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- en
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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/xxx" target="_blank">[LongAlign Paper]</a> • 🤗 <a href="https://huggingface.co/datasets/In2Training/VaLProbing-32K" target="_blank">[VaL Probing] </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 on [VaLProbing-32K](https://huggingface.co/datasets/In2Training/VaLProbing-32K/).
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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 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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## Probing Results
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