Instructions to use nihardon/fine-tuned-unit-test-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nihardon/fine-tuned-unit-test-generator with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nihardon/fine-tuned-unit-test-generator",
	filename="llama-3-8b.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nihardon/fine-tuned-unit-test-generator with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nihardon/fine-tuned-unit-test-generator:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nihardon/fine-tuned-unit-test-generator:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nihardon/fine-tuned-unit-test-generator:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nihardon/fine-tuned-unit-test-generator: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 nihardon/fine-tuned-unit-test-generator:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nihardon/fine-tuned-unit-test-generator: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 nihardon/fine-tuned-unit-test-generator:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nihardon/fine-tuned-unit-test-generator:Q4_K_M

Use Docker

docker model run hf.co/nihardon/fine-tuned-unit-test-generator:Q4_K_M

LM Studio
Jan
Ollama
How to use nihardon/fine-tuned-unit-test-generator with Ollama:
```
ollama run hf.co/nihardon/fine-tuned-unit-test-generator:Q4_K_M
```

Unsloth Studio new

How to use nihardon/fine-tuned-unit-test-generator 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 nihardon/fine-tuned-unit-test-generator 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 nihardon/fine-tuned-unit-test-generator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nihardon/fine-tuned-unit-test-generator to start chatting

Docker Model Runner
How to use nihardon/fine-tuned-unit-test-generator with Docker Model Runner:
```
docker model run hf.co/nihardon/fine-tuned-unit-test-generator:Q4_K_M
```

Lemonade

How to use nihardon/fine-tuned-unit-test-generator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nihardon/fine-tuned-unit-test-generator:Q4_K_M

Run and chat with the model

lemonade run user.fine-tuned-unit-test-generator-Q4_K_M

List all available models

lemonade list

🧠 Fine-Tuned Unit Test Generator (Llama-3-8B)

This repository contains the fine-tuned weights for the Unit Test Generator project.

📊 Training Details

Base Model: unsloth/llama-3-8b-bnb-4bit
Dataset: iamtarun/python_code_instructions_18k_alpaca
Method: QLoRA (Quantized Low-Rank Adaptation)
Framework: Unsloth + PyTorch + Hugging Face TRL

📦 Files

llama-3-8b.Q4_K_M.gguf: The quantized model optimized for CPU inference (used in the Live Demo).
adapter_model.safetensors: The raw LoRA adapters.

🔗 Live Demo

You can try the model in the interactive web app here: Launch Unit Test Generator

Downloads last month: 6

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

nihardon
/

fine-tuned-unit-test-generator

🧠 Fine-Tuned Unit Test Generator (Llama-3-8B)

📊 Training Details

📦 Files

🔗 Live Demo

Space using nihardon/fine-tuned-unit-test-generator 1