README.md

---
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.