Instructions to use jiangvi8/gemma-finetune-ai_class_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jiangvi8/gemma-finetune-ai_class_gguf with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jiangvi8/gemma-finetune-ai_class_gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jiangvi8/gemma-finetune-ai_class_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 jiangvi8/gemma-finetune-ai_class_gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf jiangvi8/gemma-finetune-ai_class_gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jiangvi8/gemma-finetune-ai_class_gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf jiangvi8/gemma-finetune-ai_class_gguf:Q8_0

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 jiangvi8/gemma-finetune-ai_class_gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jiangvi8/gemma-finetune-ai_class_gguf:Q8_0

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 jiangvi8/gemma-finetune-ai_class_gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jiangvi8/gemma-finetune-ai_class_gguf:Q8_0

Use Docker

docker model run hf.co/jiangvi8/gemma-finetune-ai_class_gguf:Q8_0

LM Studio
Jan
Ollama
How to use jiangvi8/gemma-finetune-ai_class_gguf with Ollama:
```
ollama run hf.co/jiangvi8/gemma-finetune-ai_class_gguf:Q8_0
```

Unsloth Studio

How to use jiangvi8/gemma-finetune-ai_class_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 jiangvi8/gemma-finetune-ai_class_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 jiangvi8/gemma-finetune-ai_class_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jiangvi8/gemma-finetune-ai_class_gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use jiangvi8/gemma-finetune-ai_class_gguf with Docker Model Runner:
```
docker model run hf.co/jiangvi8/gemma-finetune-ai_class_gguf:Q8_0
```

Lemonade

How to use jiangvi8/gemma-finetune-ai_class_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jiangvi8/gemma-finetune-ai_class_gguf:Q8_0

Run and chat with the model

lemonade run user.gemma-finetune-ai_class_gguf-Q8_0

List all available models

lemonade list

Uploaded finetuned model

Developed by: m1tch
License: apache-2.0
Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit

This gemma3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 2

GGUF

Model size

4B params

Architecture

gemma3

Hardware compatibility

8-bit

Inference Providers NEW

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

Model tree for jiangvi8/gemma-finetune-ai_class_gguf

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Quantized

unsloth/gemma-3-4b-it-unsloth-bnb-4bit

Quantized

(82)

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