Instructions to use cortexso/intellect-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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": "cortexso/intellect-2:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use cortexso/intellect-2 with Hermes Agent:
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 Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default cortexso/intellect-2:Q4_K_M
Run Hermes
hermes
- 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
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