Instructions to use cortexso/intellect-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortexso/intellect-2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cortexso/intellect-2",
	filename="intellect-2-q2_k.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cortexso/intellect-2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/intellect-2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/intellect-2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/intellect-2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/intellect-2:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf cortexso/intellect-2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/intellect-2:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf cortexso/intellect-2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/intellect-2:Q4_K_M

Use Docker

docker model run hf.co/cortexso/intellect-2:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/intellect-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/intellect-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortexso/intellect-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortexso/intellect-2:Q4_K_M

Ollama
How to use cortexso/intellect-2 with Ollama:
```
ollama run hf.co/cortexso/intellect-2:Q4_K_M
```

Unsloth Studio new

How to use cortexso/intellect-2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cortexso/intellect-2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cortexso/intellect-2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cortexso/intellect-2 to start chatting

Pi new

How to use cortexso/intellect-2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cortexso/intellect-2:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "intellect-2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cortexso/intellect-2 with Docker Model Runner:
```
docker model run hf.co/cortexso/intellect-2:Q4_K_M
```

Lemonade

How to use cortexso/intellect-2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/intellect-2:Q4_K_M

Run and chat with the model

lemonade run user.intellect-2-Q4_K_M

List all available models

lemonade list

Overview

Prime Intellect released INTELLECT-2, a 32 billion parameter large language model (LLM) trained through distributed reinforcement learning on globally donated GPU resources. Built on the Qwen2 architecture and fine-tuned with the prime-rl framework, INTELLECT-2 demonstrates strong performance in math, coding, and logical reasoning.

This model leverages GRPO (Generalized Reinforcement Policy Optimization) over verifiable rewards, introducing asynchronous distributed RL training with enhanced stability techniques. While its primary focus was on verifiable mathematical and coding tasks, it remains compatible with general-purpose text generation tasks.

Variants

INTELLECT-2

No	Variant	Branch	Cortex CLI command
1	INTELLECT-2 (32B)	32b	`cortex run intellect-2:32b`

Each branch includes multiple GGUF quantized versions, optimized for various hardware configurations:

INTELLECT-2-32B: q2_k, q3_k_l, q3_k_m, q3_k_s, q4_k_m, q4_k_s, q5_k_m, q5_k_s, q6_k, q8_0

Use it with Jan (UI)

Install Jan using Quickstart
Use in Jan model Hub:
```
cortexso/intellect-2
```

Use it with Cortex (CLI)

Install Cortex using Quickstart
Run the model with command:
```
cortex run intellect-2
```

Credits

Author: Prime Intellect
Converter: Menlo Research
Original License: Apache-2.0
Paper: Intellect 2 Technical Report

Downloads last month: 90

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit