Text Generation
Transformers
Safetensors
English
glm4_moe
prime-rl
verifiers
prime-intellect
reinforcement-learning
reasoning
agentic
mixture-of-experts
conversational
custom_code
Instructions to use PrimeIntellect/INTELLECT-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PrimeIntellect/INTELLECT-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PrimeIntellect/INTELLECT-3", 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", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/INTELLECT-3", trust_remote_code=True) 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
- vLLM
How to use PrimeIntellect/INTELLECT-3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PrimeIntellect/INTELLECT-3" # 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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PrimeIntellect/INTELLECT-3
- SGLang
How to use PrimeIntellect/INTELLECT-3 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" \ --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", "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" \ --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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PrimeIntellect/INTELLECT-3 with Docker Model Runner:
docker model run hf.co/PrimeIntellect/INTELLECT-3
| 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 | |
| <div align="center"> | |
| <img src="banner.png" alt="Prime Intellect Logo" /> | |
| </div> | |
| <p align="center"> | |
| <strong>INTELLECT-3: 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** is a 106B (A12B) parameter Mixture-of-Experts reasoning model post-trained from [GLM-4.5-Air-Base](https://huggingface.co/zai-org/GLM-4.5-Air-Base) using supervised fine-tuning (SFT) followed by large-scale reinforcement learning (RL). | |
|  | |
| 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). | |
| ## Evaluation | |
| INTELLECT-3 achieves best-in-class performance on math, coding, and reasoning benchmarks: | |
| | Benchmark | MATH-500 | AIME24 | AIME25 | LCB | GPQA | HLE | MMLU-Pro | | |
| |-----------|----------|---------|---------|--------|------|-----|----------| | |
| | INTELLECT-3 | **98.1** | **90.8** | **88.0** | 69.3 | 74.4 | 14.6 | 81.9 | | |
| | GLM-4.5-Air | 97.8 | 84.6 | 82.0 | 61.5 | 73.3 | 13.3 | 73.9 | | |
| | GLM-4.5 | 97.0 | 85.8 | 83.3 | 64.5 | 77.0 | 14.8 | 83.5 | | |
| | DeepSeek R1 0528 | 87.3 | 83.2 | 73.4 | 62.5 | 77.5 | 15.9 | 75.3 | | |
| | DeepSeek v3.2 | 96.8 | 88.1 | 84.7 | **71.6** | **81.4** | **17.9** | **84.6** | | |
| | GPT-O5S 120B | 96.0 | 75.8 | 77.7 | 69.9 | 70.0 | 10.6 | 67.1 | | |
| ## Model Variants | |
| | Model | HuggingFace | | |
| |-------|-------------| | |
| | INTELLECT-3 | [PrimeIntellect/INTELLECT-3](https://huggingface.co/PrimeIntellect/INTELLECT-3) | | |
| | INTELLECT-3-FP8 | [PrimeIntellect/INTELLECT-3-FP8](https://huggingface.co/PrimeIntellect/INTELLECT-3-FP8) | | |
| ## Serving with vLLM | |
| The BF16 version can be served on 2x H200s: | |
| ```bash | |
| vllm serve PrimeIntellect/INTELLECT-3 \ | |
| --tensor-parallel-size 2 \ | |
| --enable-auto-tool-choice \ | |
| --tool-call-parser qwen3_coder \ | |
| --reasoning-parser deepseek_r1 | |
| ``` | |
| The FP8 version can be served on a single H200: | |
| ```bash | |
| vllm serve PrimeIntellect/INTELLECT-3-FP8 \ | |
| --enable-auto-tool-choice \ | |
| --tool-call-parser qwen3_coder \ | |
| --reasoning-parser deepseek_r1 | |
| ``` | |
| ## Citation | |
| ```bibtex | |
| @misc{intellect3, | |
| title={INTELLECT-3: Technical Report}, | |
| author={Prime Intellect Team}, | |
| year={2025}, | |
| url={https://huggingface.co/PrimeIntellect/INTELLECT-3} | |
| } | |
| ``` | |