Instructions to use cortexso/qwen2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortexso/qwen2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cortexso/qwen2",
	filename="qwen2-7b-instruct-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/qwen2 with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

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	`cortex run qwen2:7b`

Use it with Jan (UI)

Install Jan using Quickstart
Use in Jan model Hub:
```
cortexhub/qwen2
```

Use it with Cortex (CLI)

Install Cortex using Quickstart
Run the model with command:
```
cortex run qwen2
```

Credits

Author: Qwen
Converter: Homebrew
Original License: Licence

Downloads last month: 87

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Collection including cortexso/qwen2

Local Models

Collection

16 items • Updated Mar 2 • 1