Instructions to use PrimeIntellect/INTELLECT-3-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PrimeIntellect/INTELLECT-3-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PrimeIntellect/INTELLECT-3-Base", 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-Base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/INTELLECT-3-Base", 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 Settings
- vLLM
How to use PrimeIntellect/INTELLECT-3-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PrimeIntellect/INTELLECT-3-Base" # 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-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PrimeIntellect/INTELLECT-3-Base
- SGLang
How to use PrimeIntellect/INTELLECT-3-Base 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-Base" \ --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-Base", "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-Base" \ --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-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PrimeIntellect/INTELLECT-3-Base with Docker Model Runner:
docker model run hf.co/PrimeIntellect/INTELLECT-3-Base
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README.md
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# GLM-4.5-Air-Base
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<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
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👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
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📖 Check out the GLM-4.5 <a href="https://z.ai/blog/glm-4.5" target="_blank">technical blog</a>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report</a>, and <a href="https://zhipu-ai.feishu.cn/wiki/Gv3swM0Yci7w7Zke9E0crhU7n7D" target="_blank">Zhipu AI technical documentation</a>.
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📍 Use GLM-4.5 API services on <a href="https://docs.bigmodel.cn/cn/guide/models/text/glm-4.5">Zhipu AI Open Platform</a>.
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👉 One click to <a href="https://chat.z.ai">GLM-4.5</a>.
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## Model Introduction
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library_name: transformers
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# GLM-4.5-Air-Base-Qwen-Chat-Template
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> This is a fork of the [GLM-4.5-Air-Base](https://github.com/zai-org/GLM-4.5-Air-Base) model with a custom chat template adapted from [Qwen3-Coder](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct).
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## Model Introduction
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