Instructions to use ivyface/gemma-programmersfinetune-gguf-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ivyface/gemma-programmersfinetune-gguf-1b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ivyface/gemma-programmersfinetune-gguf-1b",
	filename="gemma-3-1b-it.Q8_0.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 ivyface/gemma-programmersfinetune-gguf-1b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ivyface/gemma-programmersfinetune-gguf-1b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ivyface/gemma-programmersfinetune-gguf-1b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ivyface/gemma-programmersfinetune-gguf-1b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ivyface/gemma-programmersfinetune-gguf-1b: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 ivyface/gemma-programmersfinetune-gguf-1b:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ivyface/gemma-programmersfinetune-gguf-1b: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 ivyface/gemma-programmersfinetune-gguf-1b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ivyface/gemma-programmersfinetune-gguf-1b:Q8_0

Use Docker

docker model run hf.co/ivyface/gemma-programmersfinetune-gguf-1b:Q8_0

LM Studio
Jan
Ollama
How to use ivyface/gemma-programmersfinetune-gguf-1b with Ollama:
```
ollama run hf.co/ivyface/gemma-programmersfinetune-gguf-1b:Q8_0
```

Unsloth Studio

How to use ivyface/gemma-programmersfinetune-gguf-1b 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 ivyface/gemma-programmersfinetune-gguf-1b 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 ivyface/gemma-programmersfinetune-gguf-1b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ivyface/gemma-programmersfinetune-gguf-1b to start chatting

Atomic Chat new
Docker Model Runner
How to use ivyface/gemma-programmersfinetune-gguf-1b with Docker Model Runner:
```
docker model run hf.co/ivyface/gemma-programmersfinetune-gguf-1b:Q8_0
```

Lemonade

How to use ivyface/gemma-programmersfinetune-gguf-1b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ivyface/gemma-programmersfinetune-gguf-1b:Q8_0

Run and chat with the model

lemonade run user.gemma-programmersfinetune-gguf-1b-Q8_0

List all available models

lemonade list

gemma-programmersfinetune-gguf-1b : GGUF

This model was finetuned and converted to GGUF format using Unsloth. HumanEval test: 0.14634146341463414

Example usage:

For text only LLMs: ./llama.cpp/llama-cli -hf ivyface/gemma-programmersfinetune-gguf-1b --jinja
For multimodal models: ./llama.cpp/llama-mtmd-cli -hf ivyface/gemma-programmersfinetune-gguf-1b --jinja

Available Model files:

gemma-3-1b-it.Q8_0.gguf

Ollama

An Ollama Modelfile is included for easy deployment.

Note

The model's BOS token behavior was adjusted for GGUF compatibility. This was trained 2x faster with Unsloth

Downloads last month: 62

GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

8-bit

Inference Providers NEW

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

ivyface
/

gemma-programmersfinetune-gguf-1b

gemma-programmersfinetune-gguf-1b : GGUF

Available Model files:

Ollama

Note

Dataset used to train ivyface/gemma-programmersfinetune-gguf-1b