Nextorage/InternVL2-8B-GenCAD-Code-v2

InternVL2-8B GenCAD-Code Fine-tuned (v2)

An InternVL2-8B model fine-tuned to generate CadQuery Python code from 3D CAD model images.

Model Details

• Base Model: OpenGVLab/InternVL2-8B
• Fine-tuning Data: CADCODER/GenCAD-Code 10,000 samples (extracted from 147K)
• Training: Full fine-tuning (no LoRA), 3 epochs, best at epoch 2
• Best Validation Loss: 0.1487
• Hardware: 4x NVIDIA RTX 6000 Ada (48GB each)
• Training Framework: aiDaptive (Phison)

Performance

50-sample evaluation (stratified sampling from eval.json, unseen during training):

Metric	Base Model	Fine-tuned (v2)	Improvement
Average Loss	1.166	0.192	-83.5%
Python Syntax Valid	34/50	47/50	+38%
CadQuery Execution	1/50	46/50	+4500%
Solid Generation	0/50	46/50	-
STL Export	0/50	46/50	-

Training Configuration (v2)

Parameter	Value
Learning Rate	5e-6 (Cosine Annealing)
Effective Batch Size	32 (1/GPU × 4GPU × grad_accum=8)
Epochs	3
Max Sequence Length	4096
Weight Decay	0.05
Precision	bf16
Early Stopping	patience=2, min_delta=0.005

Usage

import torch
from transformers import AutoTokenizer, AutoModel
from PIL import Image

model_path = "Nextorage/InternVL2-8B-GenCAD-Code-v2"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).eval().cuda()

# System prompt
system_prompt = (
    "あなたはCADコード生成アシスタントです。"
    "3D CADモデルの画像が与えられた場合、そのモデルを再現する "
    "CadQuery Pythonコードを生成してください。"
    "説明は不要です。コードのみを出力してください。"
)

Limitations

• Max token length: 4096 (very complex models may be truncated)
• Trained on GenCAD-Code dataset only (specific CadQuery patterns)
• Japanese system prompt used during training

Citation

If you use this model, please cite the GenCAD-Code dataset:

@misc{gencad-code,
  title={GenCAD-Code Dataset},
  author={CADCODER},
  url={https://huggingface.co/datasets/CADCODER/GenCAD-Code}
}