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

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

78c98ce

verified ·

1 Parent(s): dea34b8

Update README.md

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README.md +18 -5

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@@ -17,13 +17,26 @@ pipeline_tag: text-generation
 ---
 # INTELLECT-3
-<div align="center">
-<img src="pi_logo.png" alt="Prime Intellect Logo" width="100px"/>
-</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
     <br>

 ---
 # INTELLECT-3
+<p align="center">
+  <picture>
+    <source
+      media="(prefers-color-scheme: dark)"
+      srcset="https://github.com/user-attachments/assets/6414bc9b-126b-41ca-9307-9e982430cde8"
+    />
+    <source
+      media="(prefers-color-scheme: light)"
+      srcset="https://github.com/user-attachments/assets/40c36e38-c5bd-4c5a-9cb3-f7b902cd155d"
+    />
+    <img
+      alt="Prime Intellect"
+      src="https://github.com/user-attachments/assets/40c36e38-c5bd-4c5a-9cb3-f7b902cd155d"
+      width="300"
+    />
+  </picture>
+</p>
 <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
     <br>