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)

# pip install -U transformers accelerate
# 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, device_map="auto")
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=256)
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
```

nielsr HF Staff commited on 1 day ago

Commit

ceda6d1

verified ·

1 Parent(s): ff39d4a

Add community evaluation results for Humanity's Last Exam, MMLU-Pro

Browse files

This PR adds community-provided evaluation results for the following benchmarks:

- **[Humanity's Last Exam](https://huggingface.co/datasets/cais/hle?eval_result=PrimeIntellect/INTELLECT-3&leaderboard_task_id=hle&leaderboard_max_params=128B&leaderboard_base_model=false)**
- **[MMLU-Pro](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro?eval_result=PrimeIntellect/INTELLECT-3&leaderboard_task_id=mmlu_pro&leaderboard_base_model=false)**

These results were extracted from the model card. This is based on the new [evaluation results feature](https://huggingface.co/docs/hub/eval-results).

*Note: This is an automated PR. Please review the evaluation results before merging.*

Files changed (2) hide show

.eval_results/hle.yaml +7 -0
.eval_results/mmlu-pro.yaml +7 -0

.eval_results/hle.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: cais/hle
+    task_id: hle
+  value: 14.6
+  source:
+    url: https://huggingface.co/PrimeIntellect/INTELLECT-3
+    name: Model Card

.eval_results/mmlu-pro.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: TIGER-Lab/MMLU-Pro
+    task_id: mmlu_pro
+  value: 81.9
+  source:
+    url: https://huggingface.co/PrimeIntellect/INTELLECT-3
+    name: Model Card