Multilingual-Multimodal-NLP/IndustrialCoder-32B-GPTQ-INT4

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
• Repository: GitHub - Industrial-Coder
• Project Page: IndustrialCoder Project Page

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.

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:

@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.