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

Libraries

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

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

pipe = pipeline("text-generation", model="Multilingual-Multimodal-NLP/IndustrialCoder", 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", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Multilingual-Multimodal-NLP/IndustrialCoder 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use Multilingual-Multimodal-NLP/IndustrialCoder 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Add metadata and improve model card for industrial code intelligence

by nielsr HF Staff - opened Mar 19

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+35

-138

nielsr

Mar 19

Hi! I'm Niels from the Hugging Face community science team.

I've opened this pull request to improve the documentation of InCoder-32B. Specifically, I have:

Added YAML metadata at the top including pipeline_tag, library_name, and license. This enables the "Use in Transformers" button and improves discoverability.
Included domain-specific tags (Verilog, CUDA, Triton, etc.) to help users find this model when searching for industrial AI tools.
Integrated a more comprehensive Model Summary and Training Pipeline description from the paper and GitHub repo to help researchers understand the "Code-Flow" process.
Ensured usage snippets are present and compatible with the transformers library.

These changes help the model reach more researchers and engineers working on hardware semantics, GPU optimization, and embedded systems.

Add metadata and improve model card for industrial code intelligencefd96970b

csjiaya changed pull request status to merged Mar 19

BingoBird

Mar 27

Thanks nielsr!

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