Instructions to use MassivDash/Gemma-4-typescript-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MassivDash/Gemma-4-typescript-coder with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MassivDash/Gemma-4-typescript-coder", filename="gemma-4-e2b-it.BF16-mmproj.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 MassivDash/Gemma-4-typescript-coder with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MassivDash/Gemma-4-typescript-coder:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MassivDash/Gemma-4-typescript-coder:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MassivDash/Gemma-4-typescript-coder:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MassivDash/Gemma-4-typescript-coder: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 MassivDash/Gemma-4-typescript-coder:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MassivDash/Gemma-4-typescript-coder: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 MassivDash/Gemma-4-typescript-coder:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MassivDash/Gemma-4-typescript-coder:Q4_K_M
Use Docker
docker model run hf.co/MassivDash/Gemma-4-typescript-coder:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use MassivDash/Gemma-4-typescript-coder with Ollama:
ollama run hf.co/MassivDash/Gemma-4-typescript-coder:Q4_K_M
- Unsloth Studio new
How to use MassivDash/Gemma-4-typescript-coder 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 MassivDash/Gemma-4-typescript-coder 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 MassivDash/Gemma-4-typescript-coder to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MassivDash/Gemma-4-typescript-coder to start chatting
- Pi new
How to use MassivDash/Gemma-4-typescript-coder with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf MassivDash/Gemma-4-typescript-coder:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "MassivDash/Gemma-4-typescript-coder:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use MassivDash/Gemma-4-typescript-coder with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf MassivDash/Gemma-4-typescript-coder:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default MassivDash/Gemma-4-typescript-coder:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use MassivDash/Gemma-4-typescript-coder with Docker Model Runner:
docker model run hf.co/MassivDash/Gemma-4-typescript-coder:Q4_K_M
- Lemonade
How to use MassivDash/Gemma-4-typescript-coder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MassivDash/Gemma-4-typescript-coder:Q4_K_M
Run and chat with the model
lemonade run user.Gemma-4-typescript-coder-Q4_K_M
List all available models
lemonade list
tags:
- gguf
- llama.cpp
- unsloth
- vision-language-model
- typescript
- web-development
Gemma-4-TypeScript-Coder : GGUF
This model is a specialized fine-tune of Gemma 4, engineered for TypeScript-centric web development, strict type safety, and modern full-stack architectures. It was trained using Unsloth Studio for maximum efficiency and precision.
π¦ TypeScript Mastery
This fine-tune specializes in:
- Strict Type Systems: Expertise in complex generics, utility types, and advanced interfaces.
- Modern Frameworks: High proficiency in Next.js, React, Vue 3, and Node.js.
- Visual Logic: Leverages vision-language capabilities to transform UI wireframes or screenshots directly into type-safe components.
- Best Practices: Focus on clean architecture and idiomatic TypeScript patterns.
π€ Credits & Acknowledgments
A major shout-out to mhhmm for the typescript-instruct-20k dataset. This robust collection of instructions allowed the model to grasp the nuances of the TypeScript ecosystem effectively.
π Usage & Inference
The model is provided in GGUF format, compatible with llama.cpp.
Example usage:
- Standard Text Chat:
llama-cli -hf MassivDash/Gemma-4-typescript-coder --jinja - Vision/Image Tasks:
llama-mtmd-cli -hf MassivDash/Gemma-4-typescript-coder --jinja
π Available Model Files
gemma-4-e2b-it.Q8_0.ggufgemma-4-e2b-it.BF16-mmproj.gguf
β οΈ Ollama Note for Vision Models
Important: Ollama currently requires a unified blob for vision models.
To use this with Ollama:
- Ensure your
Modelfileis in the same directory as the merged BF16 model. - Run:
ollama create model_name -f ./Modelfile
π Stay Connected
For more insights on AI development and fine-tuning, visit my blog: π spaceout.pl
This model was trained 2x faster with Unsloth
