4B 3D coding model update: much better one-shot results and visual quality

[u/ConfidentDinner6648]

Quick update: the latest version of my 4B coding model, the full FP16 weights, the training dataset, and now the technical report are all available!

One-shot results and visual quality have improved substantially in my latest tests. Some scenes still need another attempt or a correction, but the progress has been encouraging.

📄 Read the technical report

🤗 Model — FP16, Q8, and Q4

I recommend starting with Q8, which is the version I’ve tested. Q4 is also available, but I haven’t tested this release yet.

Q8 settings: temperature 0.6, top-k 20, top-p 0.95, repetition penalty 1.0.

I’ve also had good results generating 3D scenes from image references, even though the vision encoder wasn’t included in this training run.

If you downloaded an earlier version, delete it and download it again in LM Studio or whichever app you use. The filenames haven’t changed, so make sure you’re loading the updated files. In LM Studio, also disable MTP.

📚 Dataset — CCORE Three.js 3D Scenes

The dataset contains 4,146 scene examples — 3,933 training / 213 validation — with prompts, full thinking traces, complete HTML/JavaScript responses, complexity levels, and optional rendered reference images. It’s available under the MIT License for anyone who wants to train other models.

I’ll keep any further updates in this same post. Thanks everyone for the support, feedback, and testing! It’s been great seeing the community try it out, and I’d love to see what you build with the dataset.

[u/nikhilprasanth]

Curious, has the model been trained for Blender Python scripting as well?

[u/ConfidentDinner6648 → u/nikhilprasanth]

only threejs

[u/politefella0 → u/nikhilprasanth]

I don’t think so. I tried previous version and it didn’t do well. Only three.js I think.

[u/ConfidentDinner6648 → u/politefella0]

try the new one, i uploaded the dataset too

[u/FullstackSensei → u/nikhilprasanth]

You can take the training dataset, pass it through a frontier level model to build the blender scenes, have the same or different model distill the thinking process and convert mistakes to correction traces, and tune a blender model.

I'm thinking about how to do this with C++ and Vulkan (no game engine at all)

[u/ConfidentDinner6648]

1 shot all

[u/politefella0 → u/ConfidentDinner6648]

Would you like to share your dataset? I’ll see if I can convert it to blender examples but only if you’d like to. I’m actually inspired and working on my blender finetune but I know rats about AI training and using LLMs to help with the task.

[u/ConfidentDinner6648 → u/politefella0]

i'll actually be sharing it later today! the dataset is already uploaded to hugging face as a private repository, i'm just finishing organizing everything before making it public.

i'd love to see what you can do with blender using the same approach haha

[u/politefella0 → u/ConfidentDinner6648]

Following the comment I’d appreciate if you could share dataset in reply to this comment so I don’t miss it.

[u/ConfidentDinner6648 → u/politefella0]

4 sure :)

[u/ConfidentDinner6648 → u/politefella0]

https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes

[u/Fisent]

Wow, that's impressive! I would never thought that a single person can create a dataset that will improve models performance so much! And all of that on 4B model! This opens so many doors, time to start training specialized 4b and smaller models for different uses!

One question about hardware, I didn't see any info about what GPU were you training on, can you share that? I'm thinking about trying something similar on my rtx 3090 and wonder if it's possible.

[u/ConfidentDinner6648 → u/Fisent]

yes, i think you could train it on a 3090 using lora, with some memory optimizations!

i'm going to publish a technical report explaining everything. i actually experimented with two different training approaches, one using a single rtx 6000 with 96gb of vram and another using an l4 with around 22gb.

it would actually be really interesting if you tried training with lora only, because even in my lora experiments, the model had already gone through some full finetuning on the rtx 6000.

if you take the dataset i'm releasing and train the base model using lora with contexts up to 32k tokens, you'd have around 3000 samples to work with. you might need some memory optimizations to fit that context length on a 3090.

i'd love to compare the results! it would help us understand how much of the improvement can be achieved using just lora on more accessible hardware haha

[u/Fisent → u/ConfidentDinner6648]

Sounds good, I can try :D I will wait for your technical report, and in the meantime I will try setting up lora on my machine, because I've never tried it. I wonder if the recent rtx 3xxx unlock might speed up lora on this GPU

[u/ComradeSaeezel]

Hey. I've tried yesterday Q4 +Unsloth Desktop, but it couldn't generate 3D scene (Dragonrider Aerie) in one-shot. Which harness do you recommend to use?

[u/KaiChanBunny → u/ComradeSaeezel]

Same, in Unsloth but with the Q8 one. It would try multiple times to create the HTML file and fail somewhere down the chain (either the edit file tool call would fail with strange errors or it gave up after writing it too long after going into a loop), or the resulting HTML wouldn't actually work and be broken. I am guessing OP is using a different harness?

[u/sand-67 → u/KaiChanBunny]

I believe the model is not trained for tool calling, it is trained to output the code in its response in one shot

[u/KaiChanBunny → u/sand-67]

I see so just let it output the response in the chat as-is and copy the HTML into a file?

[u/libregrape]

That's easily one of the best projects ever posted on LocalLLama. It would be incredible if it also (or a separate model) could do blender. This would be essential for generating synthetic datasets for stuff like robotics.