Instructions to use PrimeIntellect/INTELLECT-3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PrimeIntellect/INTELLECT-3.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="PrimeIntellect/INTELLECT-3.1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PrimeIntellect/INTELLECT-3.1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/INTELLECT-3.1", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PrimeIntellect/INTELLECT-3.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PrimeIntellect/INTELLECT-3.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PrimeIntellect/INTELLECT-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PrimeIntellect/INTELLECT-3.1

SGLang

How to use PrimeIntellect/INTELLECT-3.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "PrimeIntellect/INTELLECT-3.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PrimeIntellect/INTELLECT-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "PrimeIntellect/INTELLECT-3.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PrimeIntellect/INTELLECT-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PrimeIntellect/INTELLECT-3.1 with Docker Model Runner:
```
docker model run hf.co/PrimeIntellect/INTELLECT-3.1
```

INTELLECT-3.1

File size: 2,534 Bytes

d93a0ca

---
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}
}
```