--- title: Qwen3.5-4B-3D-Coder canonical_url: "https://www.modelscope.ai/models/ccore/Qwen3.5-4B-3D-Coder" md_url: "https://www.modelscope.ai/models/ccore/Qwen3.5-4B-3D-Coder.md" repository: ccore/Qwen3.5-4B-3D-Coder last_updated: 2026-11-03 license: apache-2.0 model_type: - qwen3_5 architectures: - Qwen3_5ForConditionalGeneration base_model: - Qwen/Qwen3.5-4B base_model_relation: finetune parameters: 4.5B tensor_type: - F16 library_name: - gguf - safetensors - pytorch frameworks: - pytorch downloads: 5 stars: 0 tags: - qwen3_5 - code - threejs - 3d - preview - gguf --- # Qwen3.5-4B-3D-Coder > Qwen3.5-4B-3D-Coder - An open-source model by ccore on ModelScope. Early preview · General coding with a focus on 3D ccore/Qwen3.5-4B-3D-Coder is a 4.5B-parameter machine learning model on ModelScope. licensed under apache-2.0. derived from Qwen/Qwen3.5-4B. - **Repository**: ccore/Qwen3.5-4B-3D-Coder - **License**: apache-2.0 - **Parameters**: 4.5B - **Base model**: Qwen/Qwen3.5-4B - **Tags**: qwen3_5, code, threejs, 3d, preview, gguf - **Downloads**: 5 - **Stars**: 0 - **Last updated**: 2026-11-03 Source: https://www.modelscope.ai/models/ccore/Qwen3.5-4B-3D-Coder --- # Qwen3.5-4B-3D-Coder ### Early preview · General coding with a focus on 3D **An experiment exploring the feasibility of small language models for general-purpose coding and high-quality 3D scene generation.** This is an early preview of a 4B model I am still training, with a particular focus on creating interactive 3D scenes with Three.js. Initial hands-on results have been promising for coding in general, and especially for generating 3D scenes, so I decided to share the progress while development continues. This is an **independent fine-tune** by [CCORE](https://huggingface.co/ccore), based on [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B). **It is not an official Qwen release.** ## Optional vision support — October 30, 2026 The **Q4_K_M and Q8_0 GGUF versions can also accept image inputs** when loaded with the original Qwen3.5-4B vision encoder/projector. You can attach a reference image and ask the model to generate a Three.js scene from it. [**Download the optional BF16 vision projector**](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/resolve/main/mmproj-Qwen3.5-4B-BF16.gguf?download=true) — approximately **644 MiB**. Download it once; the same file is used with either GGUF quantization. ### Image-guided Three.js example With a reference image attached and the Three.js system prompt below, the user prompt was simply: ```text create this scene using voxels 3d ```

Input reference	Generated Three.js scene

The reference image was supplied in [community discussion #9](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/discussions/9); credit for the reference belongs to its original creator. The right-hand image is a screenshot of the generated scene. This is a qualitative example of image-guided generation; the output interprets the reference and does not reproduce every detail. ### Load the GGUF with vision in llama.cpp Download a model GGUF and the projector, then load both files explicitly: ```bash llama-server \ -m Qwen3.5-4B-3D-Coder-Q4_K_M.gguf \ --mmproj mmproj-Qwen3.5-4B-BF16.gguf \ --n-gpu-layers all --flash-attn on \ --ctx-size 65536 --parallel 1 ``` Open the server's WebUI, attach an image, and send your prompt. The setup was tested locally with **llama.cpp b11889**. For Q8_0, change the model filename to `Qwen3.5-4B-3D-Coder-Q8_0.gguf`; `--cache-type-k q8_0 --cache-type-v q8_0` can reduce KV-cache memory use and was tested locally with vision at a configured 64K context. These were short tests, not full-context stress tests. The projector uses additional memory when loaded, and image tokens share the context window with the prompt and generated code. Image support in other frontends depends on their multimodal implementation. **Projector provenance:** this is the unmodified BF16 projector from [lmstudio-community/Qwen3.5-4B-GGUF](https://huggingface.co/lmstudio-community/Qwen3.5-4B-GGUF/tree/f9f88ac3e234be915e23811a6d28ea287bdb927e), based on [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B), released under Apache 2.0. It is an optional companion file for the existing GGUF weights. ## Technical report — now available The **technical report for this training experiment is now available**. [**Read the technical report (PDF)**](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/blob/main/Qwen3.5-4B-3D-Coder_Technical_Report.pdf) · [**Download the PDF**](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/resolve/main/Qwen3.5-4B-3D-Coder_Technical_Report.pdf?download=true) ## Full FP16 weights — now available The **original FP16 weights** are now available in this repository in **Safetensors format**, alongside the Q4_K_M and Q8_0 GGUF versions. The release includes all **four weight shards**, the model configuration, tokenizer, generation configuration, chat template, and image/video processor settings. The FP16 weights were verified locally for **loading and text generation with Transformers 5.20.2**. The FP16 generation defaults use **temperature 0.6**, **top-k 20**, **top-p 0.95**, and **repetition penalty 1.0**, with caching enabled. [**Browse the FP16 files and GGUF versions**](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/tree/main) ## Training dataset — now available The dataset used to train this model is now public: [**CCORE Three.js 3D Scenes**](https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes). It contains **4,146 scene examples** (**3,933 training / 213 validation**), including prompts, full thinking traces, complete HTML/JavaScript responses, and complexity levels. Rendered reference images are available in the optional `previews` configuration. The dataset is released under the **MIT License**. ## October 27 checkpoint update Both **Q4_K_M** and **Q8_0** now contain the updated October 27 checkpoint. If you downloaded an earlier version, please download the files again to get this update. In my latest tests, **one-shot success has improved substantially**, along with **overall scene quality**. Some scenes can still require another attempt or a correction. **Generation parameters remain unchanged** for both quantizations. Use the settings listed below. The earlier fix for the layer-related issue reported by some users is also included. The comparison below shows the Rome scene across the **October 25, 26, and 27** previews. [![Rome scene progression across the October 25, 26, and 27 previews](images/roma3.png)](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/blob/main/images/roma3.png) ## Release update — October 29, 2026 The **full FP16 weights**, [**training dataset**](https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes), and [**technical report (PDF)**](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/blob/main/Qwen3.5-4B-3D-Coder_Technical_Report.pdf) are now available. The **full set of quantized versions** is also planned. ## Early examples Selected screenshots of rendered Three.js scenes: stylized architecture, gardens, voxel environments, furniture, workshop interiors, and mechanical objects, built through HTML and JavaScript. Click any image to view the original screenshot at full size.

Sakura Temple Garden	Sunset Village
Dragonrider Aerie	Voxel launch site
The Bookbinding Bench	LGA 3647 Socket
Pelican Bike No. 07	Bow-Front Secretary Desk
Kōrin Garden	Sakura bonsai

## Generation settings Use the following settings for both the **Q4_K_M** and **Q8** versions: | Parameter | Value | | --- | --- | | Temperature | 0.6–0.8 | | Top P | 0.90–0.95 | | Repeat penalty | 1.0 | | Top K | 20–40 | ### LM Studio: disable MTP > **Important:** If you're running this model in LM Studio, **disable multi-token prediction (MTP)** for the model to work properly. LM Studio enables this option by default. ## System prompt for Three.js scenes ```text Use only the Three.js core ES module from https://cdnjs.cloudflare.com/ajax/libs/three.js/0.160.0/three.module.min.js. Do not import addons or legacy examples/js paths; implement any camera controls directly inside the HTML. ``` ## Example prompts Use the system prompt above, then try one of these user prompts. ### Elephant rehabilitation habitat ```text Please create one self-contained HTML file with Three.js from cdnjs showing an elephant rehabilitation habitat. A broad sheltered yard connects water, shade, and feeding areas through accessible paths. Use the elephant's scale and movement clearance to guide the layout around the structures. Render it as a deliberate 3D voxel model built from visible cubic blocks, with clear depth, readable silhouettes, and solid spatial relationships. ``` ### Dragonrider aerie on a sea stack ```text Build a self-contained HTML scene with Three.js from cdnjs featuring a dragonrider aerie on a sea stack, with perches, a hatchery, and a windlass. The aerie occupies a flat-topped sea stack, reached by a rope windlass from the shore. Four projecting stone perches ring the central hatchery, a vaulted cave with sand nests and a smoke hole. A timber landing deck holds a hoist crane and harness racks, while switchback stairs connect the perches to a water-level boathouse. Render it as a deliberate 3D voxel model built from visible cubic blocks, with clear depth, readable silhouettes, and solid spatial relationships. ``` ### Canal lock and weir complex ```text Please create one self-contained HTML file with Three.js from cdnjs showing a canal lock and weir complex with a central island. ``` ### Animated air traffic control tower ```text Make a standalone HTML scene with Three.js from cdnjs depicting an animated air traffic control tower with rotating radar and light signals. A tapered control tower rises from a rectangular base to a cantilevered glass cab with angled glazing panels and an external catwalk. A primary surveillance radar on a roof pedestal turns continuously, and a rotating beacon sits above the cab. ``` ## Known issues The bugs observed so far have generally involved duplicate variable names, rather than major structural problems. ## ❤️ Support Future Open-Source AI Models I'm independently developing and releasing specialized AI models and datasets for 3D generation, procedural environments, and creative coding, with the goal of making these tools accessible to everyone. Training new models, generating high-quality datasets, validating thousands of examples, and running experiments require significant GPU resources, time, and effort. **If you enjoy this project and would like to help me train more models, expand the datasets, and explore new capabilities, any contribution is greatly appreciated!** My goal is to keep sharing models, weights, datasets, and improvements openly with the community. ### ☕ Support via Ko-fi [https://ko-fi.com/ccorecaio](https://ko-fi.com/ccorecaio) ### Crypto Donations **Bitcoin (BTC) — Bitcoin Network (SegWit)** `bc1qr2gnr9g0uzk8rmsq0k6xpjgg4l7r0rfyrq69e6` **Ethereum (ETH) — Ethereum Mainnet (ERC20)** `0xa17608FCdeAd9e717Cd42e04127175527b4E75CB` **Solana (SOL / USDC / USDT) — Solana Network** `GhNmYmezKi1uWGKZeVxgddjiRW2rYNHZrf84ZaMcnha` Every contribution helps fund more GPU compute, better datasets, new training runs, and future public releases. **Thank you for supporting independent open-source AI research!**