初始化项目，由ModelHub XC社区提供模型

Model: In2Training/FILM-7B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+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/2404.16811" target="_blank">[Paper]</a> • ⚓ <a href="https://huggingface.co/datasets/In2Training/VaLProbing-32K" target="_blank">[VaLProbing-32K] </a>
+</p>
+
+**FILM-7B is a 32K-context LLM that overcomes the lost-in-the-middle problem.**
+It is trained from Mistral-7B-Instruct-v0.2 by applying Information-Intensie (In2) Training.
+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.
+
+## 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
+
+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).
+
+<p align="center">
+    <img src="./figures/probing_results_new.png" width="800">
+    <br>
+</p>
+
+## Real-World Long-Context Tasks
+
+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).
+
+<p align="center">
+    <img src="./figures/real_world_long.png" width="800">
+    <br>
+</p>
+
+## Short-Context Tasks
+
+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).
+
+<p align="center">
+    <img src="./figures/short.png" width="800">
+    <br>
+</p>
+
+## 📝 Citation
+```
+@misc{an2024make,
+      title={Make Your LLM Fully Utilize the Context}, 
+      author={Shengnan An and Zexiong Ma and Zeqi Lin and Nanning Zheng and Jian-Guang Lou},
+      year={2024},
+      eprint={2404.16811},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+Disclaimer: This model is strictly for research purposes, and not an official product or service from Microsoft.