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Nianchen
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Model Details
Model Provider
Nianchen
Model Tree
Qwen/Qwen3.5-27B
Input Modalities
Output Modalities
Supported Functionality
gpt-5.4.| Benchmark | Qwen3.5-27B | gpt-5.4 | IndustryForge-27B | Δ vs base |
|---|---|---|---|---|
| CAD VQA (accuracy) | 84.10% | 87.12% | 88.23% | +4.13 |
CadQuery (passed@1e-3) | 7.82% | 72.36% | 77.88% | +70.06 |
| COM CAD (Inventor/SolidWorks, pass rate) | 28.33% | 30.00% | 73.33% | +45.00 |
CadQuery Assembly (passed@1e-3) | 0.00% | 3.85% | 15.38% | +15.38 |
| Mean | 30.06% | 48.33% | 63.71% | +33.65 |
| Model | Mean |
|---|---|
| Qwen3.5-27B | 81.86% |
| gpt-5.4 | 78.90% |
| IndustryForge-27B | 83.42% |
Benchmarks: aime26, arc, chartqa, gsm8k, gsm8k_v, math_vision, mmlu, mmmu, ocr_bench, science_qa, trivia_qa.
Six industrial-CAD sub-corpora, ~52k multimodal samples in total, mixed in a flat proportion (no hand-tuned re-weighting):
| Task family | Sub-corpora | Size |
|---|---|---|
| CAD visual understanding | cad_vqa | 4 k |
| Parametric CAD code | text2cadquery | 17 k |
| COM parts | com_2d (20 k) + com_3d (5 k) | 25 k |
| Assembly | text2cadquery-assembly (1 k) + com_assembly (5 k) | 6 k |
| Total | ~52 k |
All code samples are sandbox-executed before use; COM scripts were executed in
containerised Windows VMs (SolidWorks / Inventor / AutoCAD) and only samples
producing valid .stl / .step artefacts were kept. The corpora are
decontaminated against every downstream benchmark at both UUID and
AST-structural level.
| Item | Value |
|---|---|
| Base model | Qwen3.5-27B (multimodal, qwen3_5) |
| Method | Multi-task SFT (full-precision merged LoRA adapter) |
| PEFT | LoRA rank 8, alpha 32, all-linear; ViT + aligner frozen |
| Epochs | 1 |
| Effective batch size | 64 (per_device_train_batch_size=2, 8 GPUs) |
| Learning rate | 5e-5, cosine schedule, warmup ratio 0.05 |
| Sequence parallel | 8 |
| Sharding | DeepSpeed ZeRO-3 |
| Packing | padding_free=True (no padding tokens in loss) |
| Max sequence length | 262144 |
| Precision | bfloat16, FlashAttention (flash_attn) |
| Hardware | 1 node × 8× A100-80GB |
| Framework | ms-swift (swift sft) |
| Training steps | 6400 (max 6568; ≈0.97 epoch of the packed corpus) |
The released weights are the merged full model (no adapter needed at
inference time), serialised in 12 safetensors shards in bfloat16.
Requires transformers >= 5.2.0.
python
from transformers import AutoProcessor, AutoModelForImageTextToTextimport torchfrom PIL import Image MODEL_ID = "Nianchen/IndustryForge-27B" processor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForImageTextToText.from_pretrained( MODEL_ID, dtype=torch.bfloat16, device_map="auto",) messages = [ { "role": "user", "content": [ {"type": "image", "image": Image.open("drawing.png")}, {"type": "text", "text": "Write a CadQuery script that reproduces this part."}, ], }] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt",)inputs = {k: (v.to(model.device) if hasattr(v, "to") else v) for k, v in inputs.items()} with torch.inference_mode(): out = model.generate(**inputs, max_new_tokens=4096, do_sample=False)print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])The model was trained with the Qwen3.5 chat template and expects normal
user/assistant turns; image inputs should follow Qwen3.5-VL preprocessing
(handled by AutoProcessor).
passed@1e-3); single-part
data dominates the training distribution.bibtex
@techreport{industryforge2026, title = {IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD}, author = {Deng, Nianchen and Ai, Jiaxin and Hu, Tao and Zou, Shu and Dong, Yurui and Li, Siqi and Cai, Xinyu and Yang, Xuemeng and Wen, Licheng and Zhou, Hongbin and Zhang, Hairong and Cai, Pinlong and Shi, Botian}, year = {2026}, note = {Technical report. Model available at https://huggingface.co/Nianchen/IndustryForge-27B}}Released under the Apache License 2.0, following the license of the Qwen3.5-27B base model. Users must additionally comply with the base model's terms.
from transformers import AutoProcessor, AutoModelForImageTextToTextimport torchfrom PIL import Image MODEL_ID = "Nianchen/IndustryForge-27B" processor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForImageTextToText.from_pretrained( MODEL_ID, dtype=torch.bfloat16, device_map="auto",) messages = [ { "role": "user", "content": [ {"type": "image", "image": Image.open("drawing.png")}, {"type": "text", "text": "Write a CadQuery script that reproduces this part."}, ], }] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt",)inputs = {k: (v.to(model.device) if hasattr(v, "to") else v) for k, v in inputs.items()} with torch.inference_mode(): out = model.generate(**inputs, max_new_tokens=4096, do_sample=False)print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])@techreport{industryforge2026, title = {IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD}, author = {Deng, Nianchen and Ai, Jiaxin and Hu, Tao and Zou, Shu and Dong, Yurui and Li, Siqi and Cai, Xinyu and Yang, Xuemeng and Wen, Licheng and Zhou, Hongbin and Zhang, Hairong and Cai, Pinlong and Shi, Botian}, year = {2026}, note = {Technical report. Model available at https://huggingface.co/Nianchen/IndustryForge-27B}}