Instructions to use MassivDash/qwen3.5-4B-typescript-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MassivDash/qwen3.5-4B-typescript-coder with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MassivDash/qwen3.5-4B-typescript-coder",
	filename="Qwen3.5-4B.BF16-mmproj.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 MassivDash/qwen3.5-4B-typescript-coder 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 MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MassivDash/qwen3.5-4B-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/qwen3.5-4B-typescript-coder:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MassivDash/qwen3.5-4B-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/qwen3.5-4B-typescript-coder:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M

Use Docker

docker model run hf.co/MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M

LM Studio
Jan
Ollama
How to use MassivDash/qwen3.5-4B-typescript-coder with Ollama:
```
ollama run hf.co/MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M
```

Unsloth Studio

How to use MassivDash/qwen3.5-4B-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/qwen3.5-4B-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/qwen3.5-4B-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/qwen3.5-4B-typescript-coder to start chatting

How to use MassivDash/qwen3.5-4B-typescript-coder with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MassivDash/qwen3.5-4B-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/qwen3.5-4B-typescript-coder:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MassivDash/qwen3.5-4B-typescript-coder with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MassivDash/qwen3.5-4B-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/qwen3.5-4B-typescript-coder:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use MassivDash/qwen3.5-4B-typescript-coder with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use MassivDash/qwen3.5-4B-typescript-coder with Docker Model Runner:
```
docker model run hf.co/MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M
```

Lemonade

How to use MassivDash/qwen3.5-4B-typescript-coder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MassivDash/qwen3.5-4B-typescript-coder:Q4_K_M

Run and chat with the model

lemonade run user.qwen3.5-4B-typescript-coder-Q4_K_M

List all available models

lemonade list

qwen3.5-4B-typescript-coder / README.md

MassivDash

Update README.md

4cab5db verified 3 months ago

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	---
	tags:
	- gguf
	- llama.cpp
	- unsloth
	- vision-language-model
	- qwen
	- typescript
	license: mit
	datasets:
	- mhhmm/typescript-instruct-20k
	base_model:
	- Qwen/Qwen3.5-4B
	---

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

	## 🔗 Resources
	* Author Blog: Find more tutorials at [spaceout.pl](https://spaceout.pl)
	* Training: This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth).

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