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---
license: apache-2.0
datasets:
- In2Training/VaLProbing-32K
language:
- en
---
# FILM-7B
<p align="center">
💻 <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>
</p>
**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/).
It is trained from Mistral-7B-Instruct-v0.2 by applying Information-Intensie (In2) Training.
FILM-7B achieves SOTA-level performance on real-world long-context tasks among ~7B size LLMs and does not compromise the short-context performance.
## Model Usage
The system tempelate for FILM-7B:
```text
[INST] Below is a context and an instruction. Based on the information provided in the context, write a response for the instruction.
### Context:
{YOUR LONG CONTEXT}
### Instruction:
{YOUR QUESTION & INSTRUCTION} [/INST]
```
## Probing Results
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