Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8

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, 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
• GitHub: CSJianYang/Industrial-Coder
• Project Page: 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

pip install -U "transformers>=4.57.1" accelerate safetensors

Usage with Transformers

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

@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.