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---
library_name: transformers
tags:
- prime-rl
- verifiers
- prime-intellect
- reinforcement-learning
- reasoning
- agentic
- mixture-of-experts
license: mit
language:
- en
base_model:
- zai-org/GLM-4.5-Air-Base
pipeline_tag: text-generation
---
# INTELLECT-3.1
<div align="center">
<img src="https://huggingface.co/PrimeIntellect/INTELLECT-3/resolve/main/banner.png" alt="Prime Intellect Logo" />
</div>
<p align="center">
<strong>INTELLECT-3.1: A 100B+ MoE trained with large-scale RL</strong>
<br><br>
Trained with <a href="https://github.com/PrimeIntellect-ai/prime-rl">prime-rl</a> and <a href="https://github.com/PrimeIntellect-ai/verifiers">verifiers</a>
<br>
Environments released on <a href="https://app.primeintellect.ai/dashboard/environments">Environments Hub</a>
<br>
Read the <a href="https://primeintellect.ai/blog/intellect-3">Blog</a> & <a href="https://storage.googleapis.com/intellect-3-paper/INTELLECT_3_Technical_Report.pdf">Technical Report</a>
<br>
<a href="https://x.com/primeintellect">X</a> | <a href="https://discord.gg/RC5GvMbfDf">Discord</a> | <a href="https://app.primeintellect.ai/dashboard/create-cluster">Prime Intellect Platform</a>
</p>
## Introduction
**INTELLECT-3.1** is a 106B (A12B) parameter Mixture-of-Experts reasoning model built as a continued training of [INTELLECT-3](https://huggingface.co/PrimeIntellect/INTELLECT-3) with additional reinforcement learning on math, coding, software engineering, and agentic tasks.
Training was performed with [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) using environments built with the [verifiers](https://github.com/PrimeIntellect-ai/verifiers) library.
All training and evaluation environments are available on the [Environments Hub](https://app.primeintellect.ai/dashboard/environments).
The model, training frameworks, and environments are open-sourced under fully-permissive licenses (MIT and Apache 2.0).
For more details, see the [technical report](https://storage.googleapis.com/intellect-3-paper/INTELLECT_3_Technical_Report.pdf).
## Serving with vLLM
The model can be served on 2x H200s:
```bash
vllm serve PrimeIntellect/INTELLECT-3.1 \
--tensor-parallel-size 2 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder \
--reasoning-parser deepseek_r1
```
## Citation
```bibtex
@misc{intellect3.1,
title={INTELLECT-3.1: Technical Report},
author={Prime Intellect Team},
year={2025},
url={https://huggingface.co/PrimeIntellect/INTELLECT-3.1}
}
```
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