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
Update chat_template.jinja
Browse filesFixed Intellect-3 Template - No More 'Unknown test: sequence' Error
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chat_template.jinja
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{% macro render_extra_keys(json_dict, handled_keys) %}
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{%- for json_key in json_dict if json_key not in handled_keys %}
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{{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}
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{%- if json_dict is mapping %}
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{%- if json_dict[json_key] is string %}
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{{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson) ~ '</' ~ json_key ~ '>' }}
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