Add model card for InCoder-32B

README.md

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+---
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+- industrial-ai
+- code-generation
+---
+
+# InCoder-32B: Industrial Code Foundation Model
+
+**InCoder-32B** (Industrial-Coder-32B) is the first 32B-parameter code foundation model purpose-built for industrial code intelligence. While general code LLMs excel at standard programming tasks, they often struggle with hardware semantics, specialized language constructs, and strict resource constraints. 
+
+InCoder-32B is designed to unify code intelligence across:
+- **Chip Design** (Verilog / RTL)
+- **GPU Kernel Optimization** (CUDA / Triton)
+- **Embedded Systems** (ARM Cortex-M, STM32)
+- **Compiler Optimization** (x86-64 assembly, LLVM)
+- **3D Modeling** (CAD/CAM via CadQuery / OpenCascade)
+
+The model supports a native long-context window of up to **128K tokens**.
+
+## Resources
+- **Paper:** [InCoder-32B: Code Foundation Model for Industrial Scenarios](https://huggingface.co/papers/2603.16790)
+- **Repository:** [GitHub - Industrial-Coder](https://github.com/CSJianYang/Industrial-Coder)
+- **Project Page:** [IndustrialCoder Project Page](https://huggingface.co/Multilingual-Multimodal-NLP/IndustrialCoder)
+
+## Quickstart
+
+To use InCoder-32B with the `transformers` library, you can follow the snippet below. Note that `trust_remote_code=True` is required to load the custom model architecture.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Multilingual-Multimodal-NLP/IndustrialCoder"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+messages = [{"role": "user", "content": "Optimize this CUDA kernel for better memory coalescing."}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.85, top_k=20)
+
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+## Performance
+
+### Industrial Code Benchmarks
+InCoder-32B establishes strong open-source baselines across specialized industrial domains, often surpassing proprietary models like Claude-Sonnet-4.6 in specific tasks.
+
+| Domain | Benchmark | InCoder-32B | Claude-Sonnet-4.6 |
+|---|---|:---:|:---:|
+| **Chip Design** | VeriScope Score | 80.7 | **87.7** |
+| **GPU Optim.** | KernelBench L1/L2/L3 | **22.2/36.0/14.0** | 11.1/28.0/2.0 |
+| **3D Modeling** | CAD-Coder Compile (%) | **82.0** | 77.0 |
+| **3D Modeling** | CAD-Coder IoU | **53.5** | 32.4 |
+
+## Citation
+If you find InCoder-32B useful in your research, please cite:
+```bibtex
+@article{yang2025incoder,
+  title={InCoder-32B: Code Foundation Model for Industrial Scenarios},
+  author={Yang, Jian and Zhang, Wei and Wu, Jiajun and Cheng, Junhang and Guo, Shawn and Wang, Haowen and others},
+  journal={arXiv preprint arXiv:2603.16790},
+  year={2025}
+}
+```
+
+## Disclaimer
+The model may generate incorrect or unsafe code. Always review and test outputs in a sandboxed environment before production use. Industrial code (RTL, embedded firmware, GPU kernels) requires expert human review before deployment in physical systems or hardware synthesis.