Downloads · 30 days
34
21% of all-time downloads
bambisheng/UltraIF-8B-UltraComposer
UltraIF-8B-UltraComposer is a text generation model from bambisheng. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
UltraIF model series and data are available at 🤗 HuggingFace. - 🤖 UltraComposer - 📖 SFT Data and SFT Model - ⚖️ DPO Data and DPO Model
Downloads · 30 days
34
21% of all-time downloads
All-time downloads
165
Public
Parameters
8B
16.1 GB on disk
Likes
1
Public
Click a slice to open those files.
.safetensors16.1 GB · 100%
From the Hugging Face model README
UltraIF model series and data are available at 🤗 HuggingFace.
Also check out our 📚 Paper and 💻code
UltraIF-8B-UltraComposer is a specialized composer that can facilitate the synthesis of wild instructions with more complex and diverse constraints, fine-tuned from Llama-3.1-8B-Instruct.
UltraIF first constructs the UltraComposer by decomposing user instructions into simplified ones and constraints, along with corresponding evaluation questions. This specialized composer facilitates the synthesis of instructions with more complex and diverse constraints, while the evaluation questions ensure the correctness and reliability of the generated responses.
Then, we introduce the Generate-then-Evaluate process. This framework first uses UltraComposer to incorporate constraints into instructions and then evaluates the generated responses using corresponding evaluation questions covering various quality levels.

Format your input as follows:
[history]: {your_chat_history}
[initial query]: {your_query}
And the output will be organized in json format:
{"augmented query":.., "question":..}
For more details, check out our official implementation for UltraComposer.
<br> 📑 If you find our projects helpful to your research, please consider citing: <br>
@article{an2025ultraif,
title={UltraIF: Advancing Instruction Following from the Wild},
author={An, Kaikai and Sheng, Li and Cui, Ganqu and Si, Shuzheng and Ding, Ning and Cheng, Yu and Chang, Baobao},
journal={arXiv preprint arXiv:2502.04153},
year={2025}
}