Instructions to use PrimeIntellect/INTELLECT-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Settings

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
```

Jackmin108 commited on Nov 27, 2025

Commit

7df9506

verified ·

1 Parent(s): bf71a86

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@@ -4,6 +4,10 @@ tags:
 - prime-rl
 - verifiers
 - prime-intellect
 license: mit
 language:
 - en
@@ -14,6 +18,20 @@ pipeline_tag: text-generation
 # INTELLECT-3
 **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).

 - prime-rl
 - verifiers
 - prime-intellect
+- reinforcement-learning
+- reasoning
+- agentic
+- mixture-of-experts
 license: mit
 language:
 - en
 # INTELLECT-3
+<div align="center">
+<img src="pi_logo.png" alt="Prime Intellect Logo" width="240px"/>
+</div>
+<p align="center">
+    🚀 <strong>State-of-the-art 100B+ parameter Mixture-of-Experts model trained with large-scale reinforcement learning</strong>
+    <br><br>
+    📚 Trained with <a href="https://github.com/PrimeIntellect-ai/prime-rl">prime-rl</a> infra and <a href="https://github.com/PrimeIntellect-ai/verifiers">verifiers</a> environments | 🌐 Environments on <a href="https://app.primeintellect.ai/dashboard/environments">Environments Hub</a>
+    <br>
+    📖 Read the <a href="https://primeintellect.ai/blog/intellect-3">Technical Report</a> | 💬 Join our <a href="https://discord.gg/RC5GvMbfDf">Discord</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).