Add model card and metadata for InCoder-32B

Hi! I'm Niels from the Hugging Face community science team. I noticed this repository was missing a model card.

This PR adds a comprehensive model card for InCoder-32B, including:
- Relevant metadata (`pipeline_tag`, `library_name`).
- A link to the paper [InCoder-32B: Code Foundation Model for Industrial Scenarios](https://huggingface.co/papers/2603.16790).
- Information about the model's specialized capabilities in industrial domains like chip design, GPU optimization, and embedded systems.
- A quickstart code snippet for using the model with the `transformers` library.
- Performance benchmarks and citation details.

This will help users discover and use your model more effectively on the Hub!

README.md

@@ -0,0 +1,89 @@
+---
+library_name: transformers
+pipeline_tag: text-generation
+license: other
+tags:
+- code
+- industrial-ai
+- code-generation
+---
+
+# InCoder-32B: Industrial Code Foundation Model
+
+[InCoder-32B](https://huggingface.co/papers/2603.16790) (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, InCoder-32B is specifically designed to address challenges in hardware semantics, specialized language constructs, and strict resource constraints.
+
+## Model Description
+InCoder-32B unifies code intelligence across several industrial engineering domains:
+- **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**.
+
+### Links
+- **Paper**: [InCoder-32B: Code Foundation Model for Industrial Scenarios](https://huggingface.co/papers/2603.16790)
+- **GitHub**: [CSJianYang/Industrial-Coder](https://github.com/CSJianYang/Industrial-Coder)
+- **Project Page**: [IndustrialCoder](https://huggingface.co/Multilingual-Multimodal-NLP/IndustrialCoder)
+
+## Performance Highlights
+InCoder-32B leads open-weight baselines across industrial domains and surpasses proprietary models like Claude-Sonnet-4.6 on specific benchmarks such as CAD-Coder IoU and KernelBench.
+
+| Domain | Benchmark | InCoder-32B | Claude-Sonnet-4.6 |
+|---|---|:---:|:---:|
+| **Chip Design** | RealBench Func@1 (Mod) | **62.7** | 37.2 |
+| **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 |
+| **Code Optim.** | SuperCoder Acc | **91.0** | 88.0 |
+
+## Quickstart
+
+### Installation
+```bash
+pip install -U "transformers>=4.57.1" accelerate safetensors
+```
+
+### Usage with Transformers
+```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))
+```
+
+## Training Pipeline
+The model is trained via a three-stage **Code-Flow** pipeline:
+1. **Pre-training & Annealing**: General pre-training followed by curated industrial code annealing.
+2. **Mid-training**: Progressive context extension from 8K to 128K tokens using synthetic industrial reasoning data.
+3. **Post-training**: Execution-grounded SFT with 2.5M samples and feedback-driven repair trajectories.
+
+## Citation
+```bibtex
+@article{yang2025incoder32b,
+  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 Gu, Weicheng and Du, Yaxin and Li, Joseph and Xu, Fanglin 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.