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

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 Settings

llama.cpp

How to use llmware/qwen2.5-vl-3b-instruct-gguf 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 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 serve -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

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

Atomic Chat new
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

Model Card Contact

llmware on hf

llmware website

Downloads last month: 118

GGUF

Model size

3B params

Architecture

qwen2vl

Hardware compatibility

4-bit

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Model tree for llmware/qwen2.5-vl-3b-instruct-gguf

Base model

Qwen/Qwen2.5-VL-3B-Instruct

Quantized

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