Instructions to use llmware/qwen2.5-vl-3b-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use llmware/qwen2.5-vl-3b-instruct-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="llmware/qwen2.5-vl-3b-instruct-gguf", filename="Qwen2.5-VL-3B-Instruct-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use llmware/qwen2.5-vl-3b-instruct-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf llmware/qwen2.5-vl-3b-instruct-gguf: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 llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf llmware/qwen2.5-vl-3b-instruct-gguf: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 llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M
Use Docker
docker model run hf.co/llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use llmware/qwen2.5-vl-3b-instruct-gguf with Ollama:
ollama run hf.co/llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M
- Unsloth Studio new
How to use llmware/qwen2.5-vl-3b-instruct-gguf 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 llmware/qwen2.5-vl-3b-instruct-gguf 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 llmware/qwen2.5-vl-3b-instruct-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for llmware/qwen2.5-vl-3b-instruct-gguf to start chatting
- Docker Model Runner
How to use llmware/qwen2.5-vl-3b-instruct-gguf with Docker Model Runner:
docker model run hf.co/llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M
- Lemonade
How to use llmware/qwen2.5-vl-3b-instruct-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull llmware/qwen2.5-vl-3b-instruct-gguf:Q4_K_M
Run and chat with the model
lemonade run user.qwen2.5-vl-3b-instruct-gguf-Q4_K_M
List all available models
lemonade list
qwen2.5-vl-3b-instruct-gguf
qwen2.5-vl-3b-instruct-gguf is a GGUF Q4_K_M quantized version of [Qwen2.5-VL-Instruct](https://www.huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct, providing a fast, small inference implementation, optimized for AI PCs.
Model Description
- Developed by: Qwen
- Quantized by: unsloth
- Model type: qwen2vl
- Parameters: 3 billion
- Model Parent: Qwen/Qwen2.5-VL-3B-Instruct
- Language(s) (NLP): English
- License: Apache 2.0
- Uses: Chat, general-purpose LLM
- Quantization: int4
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