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

dea34b8

verified ·

1 Parent(s): 597e1d9

Update README.md

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@@ -38,17 +38,18 @@ pipeline_tag: text-generation
 ![bench](temp_bench.png)
-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](PAPER_LINK_PLACEHOLDER).
 ## Evaluation
 INTELLECT-3 achieves best-in-class performance on math, coding, and reasoning benchmarks:
-| Benchmark | MATH-500 | AIME24 | AIME25 | LCB²² | 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 |

 ![bench](temp_bench.png)
+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://huggingface.co/PrimeIntellect/INTELLECT-3).
 ## 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 |