Instructions to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8

SGLang

How to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8 with Docker Model Runner:
```
docker model run hf.co/Multilingual-Multimodal-NLP/IndustrialCoder-32B-FP8
```

IndustrialCoder-32B-FP8 / README.md

nielsr HF Staff

Add model card and metadata for InCoder-32B

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	---
	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.