Instructions to use cortexso/qwen2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cortexso/qwen2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/qwen2:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/qwen2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/qwen2:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/qwen2: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/qwen2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/qwen2: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/qwen2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/qwen2:Q4_K_M
Use Docker
docker model run hf.co/cortexso/qwen2:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/qwen2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/qwen2" # 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/qwen2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/qwen2:Q4_K_M
- Ollama
How to use cortexso/qwen2 with Ollama:
ollama run hf.co/cortexso/qwen2:Q4_K_M
- Unsloth Studio
How to use cortexso/qwen2 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/qwen2 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/qwen2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/qwen2 to start chatting
- Docker Model Runner
How to use cortexso/qwen2 with Docker Model Runner:
docker model run hf.co/cortexso/qwen2:Q4_K_M
- Lemonade
How to use cortexso/qwen2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/qwen2:Q4_K_M
Run and chat with the model
lemonade run user.qwen2-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,180 Bytes
7307df7 44b8b9e 7307df7 e2c6376 7307df7 e8301fb 7307df7 e8301fb 7307df7 e8301fb 7307df7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ---
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- cortex.cpp
---
## Overview
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model.
## Variants
| No | Variant | Cortex CLI command |
| --- | --- | --- |
| 1 | [Qwen2-7b](https://huggingface.co/cortexso/qwen2/tree/7b) | `cortex run qwen2:7b` |
## Use it with Jan (UI)
1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
2. Use in Jan model Hub:
```bash
cortexhub/qwen2
```
## Use it with Cortex (CLI)
1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
2. Run the model with command:
```bash
cortex run qwen2
```
## Credits
- **Author:** Qwen
- **Converter:** [Homebrew](https://www.homebrew.ltd/)
- **Original License:** [Licence](https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE) |