MassivDash/qwen3.5-4B-typescript-coder

Qwen3.5-4B-TypeScript-Coder : GGUF

This model is a high-performance fine-tune of Qwen 3.5 4B, specifically optimized for TypeScript development, architectural reasoning, and full-stack engineering. Fine-tuned using Unsloth Studio, it leverages Qwen 3.5's native multimodal foundation to provide industry-leading code generation and visual-to-code capabilities.

🚀 Key Features

• —TypeScript Specialization: Deeply tuned for strict type safety, Generics, and modern frameworks like React, Next.js, and Node.js.
• —Visual-to-Code: Capable of understanding UI screenshots and system diagrams to generate clean, type-safe logic.
• —Optimized Inference: Converted to GGUF for low-latency performance on local hardware.

🤝 Dataset Credits

This model was trained using the [typescript-instruct-20k](https://huggingface.co/datasets/mhhmm/typescript-instruct-20k) dataset by mhhmm. This high-quality data allows the model to handle everything from simple scripts to enterprise-level refactoring.

📂 Model Files & Inference

Compatible with llama.cpp and other GGUF-supported runners.

• —High-Precision: qwen3.5-4b-typescript.Q8_0.gguf
• —Vision Projector: qwen3.5-4b-typescript.BF16-mmproj.gguf

Example usage:

• —CLI Chat: llama-cli -hf MassivDash/qwen3.5-4B-typescript-coder --jinja
• —Vision Tasks: llama-mtmd-cli -hf MassivDash/qwen3.5-4B-typescript-coder --jinja

⚠️ Ollama Integration

To use this multimodal model in Ollama:

1. 1.Create a Modelfile in your local directory.
2. 2.Run: ollama create qwen-ts-coder -f ./Modelfile

🔗 Resources

• —Author Blog: Find more tutorials at spaceout.pl
• —Training: This model was trained 2x faster with Unsloth.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>