Instructions to use kevin-hu-lab/finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kevin-hu-lab/finetuning with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kevin-hu-lab/finetuning", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kevin-hu-lab/finetuning 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 kevin-hu-lab/finetuning:F16
# Run inference directly in the terminal:
llama cli -hf kevin-hu-lab/finetuning:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kevin-hu-lab/finetuning:F16
# Run inference directly in the terminal:
llama cli -hf kevin-hu-lab/finetuning: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 kevin-hu-lab/finetuning:F16
# Run inference directly in the terminal:
./llama-cli -hf kevin-hu-lab/finetuning: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 kevin-hu-lab/finetuning:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kevin-hu-lab/finetuning:F16

Use Docker

docker model run hf.co/kevin-hu-lab/finetuning:F16

LM Studio
Jan
Ollama
How to use kevin-hu-lab/finetuning with Ollama:
```
ollama run hf.co/kevin-hu-lab/finetuning:F16
```

Unsloth Studio

How to use kevin-hu-lab/finetuning 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 kevin-hu-lab/finetuning 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 kevin-hu-lab/finetuning to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kevin-hu-lab/finetuning to start chatting

Docker Model Runner
How to use kevin-hu-lab/finetuning with Docker Model Runner:
```
docker model run hf.co/kevin-hu-lab/finetuning:F16
```

Lemonade

How to use kevin-hu-lab/finetuning with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kevin-hu-lab/finetuning:F16

Run and chat with the model

lemonade run user.finetuning-F16

List all available models

lemonade list

Atomic Chat

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports	Free Notebooks	Performance	Memory use
Llama 3 (8B)	▶️ Start for free	2x faster	60% less
Mistral (7B)	▶️ Start for free	2.2x faster	73% less
Gemma (7B)	▶️ Start for free	2.4x faster	71% less
ORPO	▶️ Start for free	1.9x faster	43% less
DPO Zephyr	▶️ Start for free	1.9x faster	43% less
Phi-3 (3.8B)	▶️ Start for free	2x faster	50% less
TinyLlama	▶️ Start for free	3.9x faster	74% less

Downloads last month: 11

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

16-bit

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