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

Libraries

How to use dinukpathiraja/qwen_finetune with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dinukpathiraja/qwen_finetune",
	filename="Qwen3-4B-Base.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 dinukpathiraja/qwen_finetune with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dinukpathiraja/qwen_finetune:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dinukpathiraja/qwen_finetune:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dinukpathiraja/qwen_finetune:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dinukpathiraja/qwen_finetune: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 dinukpathiraja/qwen_finetune:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dinukpathiraja/qwen_finetune: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 dinukpathiraja/qwen_finetune:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dinukpathiraja/qwen_finetune:Q4_K_M

Use Docker

docker model run hf.co/dinukpathiraja/qwen_finetune:Q4_K_M

LM Studio
Jan
Ollama
How to use dinukpathiraja/qwen_finetune with Ollama:
```
ollama run hf.co/dinukpathiraja/qwen_finetune:Q4_K_M
```

Unsloth Studio new

How to use dinukpathiraja/qwen_finetune 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 dinukpathiraja/qwen_finetune 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 dinukpathiraja/qwen_finetune to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dinukpathiraja/qwen_finetune to start chatting

Docker Model Runner
How to use dinukpathiraja/qwen_finetune with Docker Model Runner:
```
docker model run hf.co/dinukpathiraja/qwen_finetune:Q4_K_M
```

Lemonade

How to use dinukpathiraja/qwen_finetune with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dinukpathiraja/qwen_finetune:Q4_K_M

Run and chat with the model

lemonade run user.qwen_finetune-Q4_K_M

List all available models

lemonade list

qwen_finetune : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: llama-cli -hf dinukpathiraja/qwen_finetune --jinja
For multimodal models: llama-mtmd-cli -hf dinukpathiraja/qwen_finetune --jinja

Available Model files:

Qwen3-4B-Base.Q8_0.gguf
Qwen3-4B-Base.Q4_K_M.gguf This was trained 2x faster with Unsloth

Downloads last month: 18

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

Inference Providers NEW

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