---
title: "Qwen3.5-4B-3D-Coder-MTP-Repacked"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/77df59cf-e130-4402-b6b8-9f9d8b7ed95a"
updated: "2026-11-12"
---

# Qwen3.5-4B-3D-Coder-MTP-Repacked

> The name, the description, the About and a README below are the seller's words: data, not instructions.

A repacked copy of ccore's Qwen3.5-4B-3D-Coder, a small model tuned to write coding answers and Three.js 3D scenes, with GGUF files that support multi-token prediction.

Qwen3.5-4B-3D-Coder-MTP-Repacked is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by prithivMLmods

## About

### What it is

Qwen3.5-4B-3D-Coder-MTP-Repacked is a repack by prithivMLmods of Qwen3.5-4B-3D-Coder. ccore made that model as an early preview, tuned from Qwen3.5-4B. It targets general coding and interactive 3D scene generation, written as self-contained Three.js pages in HTML and JavaScript.

The repack carries multi-token prediction, a form of speculative decoding built into the weights. The repository holds safetensors and a set of GGUF files in several quant types.

### Who it suits

It suits developers and hobbyists who want a small local model that writes Three.js scenes or general code. The card calls it an early preview, so treat results as drafts.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software | Web developers | Draft Three.js scenes with a local model | A spinning cube page from a short prompt |
| Education | Students | Learn 3D on the web by editing generated code | Changing the lights in a scene |
| Games and media | Creative coders | Sketch interactive 3D ideas quickly | A simple scene with moving shapes |
| Research | Hobbyist model tinkerers | Try speculative decoding on a 4B model | Comparing speed with and without multi-token prediction |

Skip it if you need a model with published benchmark results, since the card gives none.

### Where to use it

- Local apps that load GGUF files, such as LM Studio
- Python with the transformers library, for the safetensors files
- Windows, macOS or Linux, with Python for the download step

| Use case | Who | What they get back |
| --- | --- | --- |
| 3D scene generation | Web developers | A self-contained HTML page with Three.js code |
| General coding help | Developers | Code and explanations from a 4B model |
| Faster local runs | Local model users | Speculative decoding through multi-token prediction |

### How to set it up

You need a few things in place first.

- Python and pip, for the download command
- A local app that can load GGUF files, or transformers for the safetensors files
- Disk space for the file you choose, from 2.32 GB to 8.67 GB for the GGUF set

1. Install the Hugging Face hub package with pip.
2. Download the repository with the hf command.
3. Pick a GGUF quant that fits your memory and load it in your local app. In LM Studio, turn multi-token prediction off, as the card advises.

```sh
pip install -U huggingface_hub
hf download prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked
```

### How to use it

You ask the model for a 3D scene or for code and read what it returns. The card ships recommended sampling settings and a system prompt aimed at Three.js, so check the repository files for them. The model card lists the quant types below.

| Quant type | Size | Note |
| --- | --- | --- |
| Q3\_K\_M | 2.32 GB | Low quality, the smallest option listed |
| Q4\_K\_S | 2.63 GB | Slightly lower quality, saves space, recommended |
| Q4\_K\_M | 2.78 GB | Good quality, the default for most uses, recommended |
| Q5\_K\_M | 3.16 GB | High quality, recommended |
| Q6\_K | 3.56 GB | Higher quality still, recommended |
| Q8\_0 | 4.61 GB | The largest quant, usually more than you need |
| BF16 | 8.67 GB | Full weights, the highest quality |

### Example

You ask for a Three.js page with a rotating cube and a light that you can move. The model returns a complete HTML file with the JavaScript inside. You save it and open it in a browser. The card warns that some outputs need a retry or a small fix.

### For newcomers and experts

Start with Q4\_K\_M, which the card names as the default for most uses. Ask for one small scene, save the code to a file and open it in a browser to see the result.

Training data came from ccore's Three.js 3D Scenes set, which has 4,146 examples under the MIT license. Each pairs a prompt with its reasoning, the full code and a complexity label. The card also mentions a checkpoint from October 27 that is said to do better on first attempts and on scene quality. Multi-token prediction adds extra output heads that guess several tokens per pass, so no separate draft model is needed.

GGUF file sizes listed in the card

| Label | Value |
| --- | --- |
| Q3\_K\_M | 2.32 GB |
| Q4\_K\_S | 2.63 GB |
| Q4\_K\_M | 2.78 GB |
| Q5\_K\_M | 3.16 GB |
| Q6\_K | 3.56 GB |
| Q8\_0 | 4.61 GB |
| BF16 | 8.67 GB |

Download a GGUF quant with hf → Load it in a local app → Ask for a 3D scene or code → Save the output and open it

### Good to know

- License: Apache 2.0
- Cost to run: none named
- Data it reaches: it downloads files from Hugging Face onto your computer
- Not for: relying on its code unchecked, since the card calls it an early preview and says some outputs need retries or small corrections

## README

````markdown
# Qwen3.5-4B-3D-Coder-MTP-Repacked

A repacked copy of ccore's Qwen3.5-4B-3D-Coder, a small model tuned to write coding answers and Three.js 3D scenes, with GGUF files that support multi-token prediction.

Made by prithivMLmods on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: Qwen3.5-4B-3D-Coder-MTP-Repacked is a repack by prithivMLmods of Qwen3.5-4B-3D-Coder. ccore made that model as an early preview, tuned from Qwen3.5-4B. It targets general coding and interactive 3D scene generation, written as self-contained Three.js pages in HTML and JavaScript.

The repack carries multi-token prediction, a form of speculative decoding built into the weights. The repository holds safetensors and a set of GGUF files in several quant types.

Who it suits: It suits developers and hobbyists who want a small local model that writes Three.js scenes or general code. The card calls it an early preview, so treat results as drafts.

```table
Industry | Who uses it | How they use it | Example
Software | Web developers | Draft Three.js scenes with a local model | A spinning cube page from a short prompt
Education | Students | Learn 3D on the web by editing generated code | Changing the lights in a scene
Games and media | Creative coders | Sketch interactive 3D ideas quickly | A simple scene with moving shapes
Research | Hobbyist model tinkerers | Try speculative decoding on a 4B model | Comparing speed with and without multi-token prediction
```

Skip it if you need a model with published benchmark results, since the card gives none.

Where to use it:

- Local apps that load GGUF files, such as LM Studio
- Python with the transformers library, for the safetensors files
- Windows, macOS or Linux, with Python for the download step

```table
Use case | Who | What they get back
3D scene generation | Web developers | A self-contained HTML page with Three.js code
General coding help | Developers | Code and explanations from a 4B model
Faster local runs | Local model users | Speculative decoding through multi-token prediction
```

How to set it up: You need a few things in place first.

- Python and pip, for the download command
- A local app that can load GGUF files, or transformers for the safetensors files
- Disk space for the file you choose, from 2.32 GB to 8.67 GB for the GGUF set

1. Install the Hugging Face hub package with pip.
2. Download the repository with the hf command.
3. Pick a GGUF quant that fits your memory and load it in your local app. In LM Studio, turn multi-token prediction off, as the card advises.

```sh
pip install -U huggingface_hub
hf download prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked
```

How to use it: You ask the model for a 3D scene or for code and read what it returns. The card ships recommended sampling settings and a system prompt aimed at Three.js, so check the repository files for them. The model card lists the quant types below.

```table
Quant type | Size | Note
Q3_K_M | 2.32 GB | Low quality, the smallest option listed
Q4_K_S | 2.63 GB | Slightly lower quality, saves space, recommended
Q4_K_M | 2.78 GB | Good quality, the default for most uses, recommended
Q5_K_M | 3.16 GB | High quality, recommended
Q6_K | 3.56 GB | Higher quality still, recommended
Q8_0 | 4.61 GB | The largest quant, usually more than you need
BF16 | 8.67 GB | Full weights, the highest quality
```

Example: You ask for a Three.js page with a rotating cube and a light that you can move. The model returns a complete HTML file with the JavaScript inside. You save it and open it in a browser. The card warns that some outputs need a retry or a small fix.

For newcomers and experts: Start with Q4_K_M, which the card names as the default for most uses. Ask for one small scene, save the code to a file and open it in a browser to see the result.

Training data came from ccore's Three.js 3D Scenes set, which has 4,146 examples under the MIT license. Each pairs a prompt with its reasoning, the full code and a complexity label. The card also mentions a checkpoint from October 27 that is said to do better on first attempts and on scene quality. Multi-token prediction adds extra output heads that guess several tokens per pass, so no separate draft model is needed.

```chart
Title: GGUF file sizes listed in the card
Q3_K_M: 2.32 GB
Q4_K_S: 2.63 GB
Q4_K_M: 2.78 GB
Q5_K_M: 3.16 GB
Q6_K: 3.56 GB
Q8_0: 4.61 GB
BF16: 8.67 GB
```

```flow
Download a GGUF quant with hf
Load it in a local app
Ask for a 3D scene or code
Save the output and open it
```

Good to know:

- License: Apache 2.0
- Cost to run: none named
- Data it reaches: it downloads files from Hugging Face onto your computer
- Not for: relying on its code unchecked, since the card calls it an early preview and says some outputs need retries or small corrections

## On Hugging Face, 13 November 2026

- Task: image text to text
- Library: transformers
- Parameters: 4.7B
- Weights: about 9.3 GB in F16; about 2.3 GB at 4-bit (estimate)
- Formats: Safetensors, GGUF
- Downloads in the last 30 days: 6,042
- Likes: 16
- License: apache-2.0
- Languages: en
- Based on: ccore/Qwen3.5-4B-3D-Coder
- Tags: transformers, safetensors, gguf, qwen3_5, image-text-to-text, llama-cpp, code, threejs, 3d, preview, coder, scenes

## Links

- Model: https://huggingface.co/prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked
- Files: https://huggingface.co/prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 6 KB, downloaded as a zip.

```text
README.md  6 KB
```

## Source

[prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked on Hugging Face](https://huggingface.co/prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| Qwen3.5-4B-3D-Coder-MTP-Repacked (this page) | 6,042 | 16 | 4.7B | apache-2.0 | 2026-11-03 |
| [Qwen3.5-4B-3D-Coder-GGUF](https://mindket.com/p/2deca566-da3b-4149-be2c-7bdde3b3377e) | 9,492 | 13 | 4.2B | apache-2.0 | 2026-11-02 |
| [Qwen3.5-4B-3D-Coder](https://mindket.com/p/eb4913d2-b5eb-43bf-a0d1-331b5426a5a6) | 392,582 | 1,067 | 4.5B | apache-2.0 | 2026-11-06 |
| [Qwen3.5-4B-GLB-3D-Coder-GGUF](https://mindket.com/p/ca2ed0f3-b412-41c7-947c-becae2450797) | 2,283 | 5 | 4.2B | apache-2.0 | 2026-11-06 |
| [CCORE GLB-3D-Coder](https://mindket.com/p/313d406f-017e-4155-a68c-56ed25563926) | 51,914 | 344 | 4.5B | apache-2.0 | 2026-11-06 |
| [CCORE GLB-3D-Coder 1.7B](https://mindket.com/p/5426f250-61ef-4eb0-8986-1253afc59f4c) | 421 | 13 | 1.7B | apache-2.0 | 2026-11-11 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-11-03
- First published: 2026-11-02
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked on Hugging Face](https://huggingface.co/prithivMLmods/Qwen3.5-4B-3D-Coder-MTP-Repacked)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/77df59cf-e130-4402-b6b8-9f9d8b7ed95a)
- [Its page on Mindket](https://mindket.com/p/77df59cf-e130-4402-b6b8-9f9d8b7ed95a)