Instructions to use lm-kit/qwen2-vl-8.3b-instruct-lmk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lm-kit/qwen2-vl-8.3b-instruct-lmk 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 lm-kit/qwen2-vl-8.3b-instruct-lmk:F16
# Run inference directly in the terminal:
llama cli -hf lm-kit/qwen2-vl-8.3b-instruct-lmk:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lm-kit/qwen2-vl-8.3b-instruct-lmk:F16
# Run inference directly in the terminal:
llama cli -hf lm-kit/qwen2-vl-8.3b-instruct-lmk:F16

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 lm-kit/qwen2-vl-8.3b-instruct-lmk:F16
# Run inference directly in the terminal:
./llama-cli -hf lm-kit/qwen2-vl-8.3b-instruct-lmk:F16

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 lm-kit/qwen2-vl-8.3b-instruct-lmk:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lm-kit/qwen2-vl-8.3b-instruct-lmk:F16

Use Docker

docker model run hf.co/lm-kit/qwen2-vl-8.3b-instruct-lmk:F16

LM Studio
Jan
Ollama
How to use lm-kit/qwen2-vl-8.3b-instruct-lmk with Ollama:
```
ollama run hf.co/lm-kit/qwen2-vl-8.3b-instruct-lmk:F16
```

Unsloth Studio

How to use lm-kit/qwen2-vl-8.3b-instruct-lmk 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 lm-kit/qwen2-vl-8.3b-instruct-lmk 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 lm-kit/qwen2-vl-8.3b-instruct-lmk to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lm-kit/qwen2-vl-8.3b-instruct-lmk to start chatting

Docker Model Runner
How to use lm-kit/qwen2-vl-8.3b-instruct-lmk with Docker Model Runner:
```
docker model run hf.co/lm-kit/qwen2-vl-8.3b-instruct-lmk:F16
```

Lemonade

How to use lm-kit/qwen2-vl-8.3b-instruct-lmk with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lm-kit/qwen2-vl-8.3b-instruct-lmk:F16

Run and chat with the model

lemonade run user.qwen2-vl-8.3b-instruct-lmk-F16

List all available models

lemonade list

Atomic Chat

Qwen2-VL-7B

Original model: https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct

This repository contains the Qwen2-VL-7B-Instruct model stored in an .lmk file format, designed for inference with the LM-Kit SDK.

Downloads last month: 27

GGUF

Model size

8B params

Architecture

qwen2vl

Hardware compatibility

16-bit

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