Instructions to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked 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 prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M

SGLang

How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M
```

Lemonade

How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-3D-Coder-MTP-Repacked-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked: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 prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked: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 "prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked: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"

prithivMLmods commited on 1 day ago

Commit

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@@ -21,3 +21,9 @@ library_name: transformers
 # **Qwen3.5-4B-3D-Coder-MTP-Repacked**
 > **Qwen3.5-4B-3D-Coder** is an early-preview independent fine-tune of `Qwen3.5-4B` by [ccore](https://huggingface.co/ccore), exploring general-purpose coding and interactive 3D scene generation using self-contained Three.js HTML and JavaScript. It was trained on the MIT-licensed ccore Three.js 3D Scenes dataset, containing 4,146 examples with prompts, reasoning traces, complete code, and complexity labels. The repository includes FP16 safetensors, Q4_K_M and Q8_0 GGUF builds, and a technical report. An October 27 checkpoint reportedly improves one-shot generation and scene quality. No quantitative benchmark results are provided; evaluation is demonstrated through sample 3D scenes. Released under Apache-2.0, it includes recommended sampling settings and a Three.js-focused system prompt. LM Studio users should disable multi-token prediction. Some generations may still require retries or minor code corrections.

 # **Qwen3.5-4B-3D-Coder-MTP-Repacked**
 > **Qwen3.5-4B-3D-Coder** is an early-preview independent fine-tune of `Qwen3.5-4B` by [ccore](https://huggingface.co/ccore), exploring general-purpose coding and interactive 3D scene generation using self-contained Three.js HTML and JavaScript. It was trained on the MIT-licensed ccore Three.js 3D Scenes dataset, containing 4,146 examples with prompts, reasoning traces, complete code, and complexity labels. The repository includes FP16 safetensors, Q4_K_M and Q8_0 GGUF builds, and a technical report. An October 27 checkpoint reportedly improves one-shot generation and scene quality. No quantitative benchmark results are provided; evaluation is demonstrated through sample 3D scenes. Released under Apache-2.0, it includes recommended sampling settings and a Three.js-focused system prompt. LM Studio users should disable multi-token prediction. Some generations may still require retries or minor code corrections.
+> [!NOTE]
+> MTP — Speculative Decoding: Yes
+> [!NOTE]
+Multi-Token Prediction (MTP) GGUF is a specialized GGUF model file format extension that integrates speculative decoding directly into the model weights to significantly accelerate local inference.  Unlike traditional speculative decoding which requires a separate, smaller "draft" model, MTP GGUF files include additional output heads within the main model architecture that predict multiple future tokens in a single forward pass.