Instructions to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-AWQ-INT4 with Transformers:

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

pipe = pipeline("text-generation", model="Multilingual-Multimodal-NLP/IndustrialCoder-32B-AWQ-INT4", 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-AWQ-INT4", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-AWQ-INT4 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-AWQ-INT4"
# 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-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Multilingual-Multimodal-NLP/IndustrialCoder-32B-AWQ-INT4

SGLang

How to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-AWQ-INT4 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-AWQ-INT4" \
    --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-AWQ-INT4",
		"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-AWQ-INT4" \
        --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-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Multilingual-Multimodal-NLP/IndustrialCoder-32B-AWQ-INT4 with Docker Model Runner:
```
docker model run hf.co/Multilingual-Multimodal-NLP/IndustrialCoder-32B-AWQ-INT4
```

Add model card for InCoder-32B

by nielsr HF Staff - opened Mar 19

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+95

-0

Files changed (1) hide show

README.md +95 -0

README.md ADDED Viewed

	@@ -0,0 +1,95 @@

+---
+library_name: transformers
+pipeline_tag: text-generation
+license: other
+tags:
+- code
+- industrial-code
+- long-context
+---
+# 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, their performance often degrades in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints.
+InCoder-32B unifies 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**.
+- **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](https://huggingface.co/Multilingual-Multimodal-NLP/IndustrialCoder)
+## Performance
+### General Code Benchmarks
+| Model | Size | HumanEval | HumanEval+ | MBPP | MBPP+ | BCB Full | BCB Hard |
+|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| Qwen2.5-Coder-32B-Instruct | 32B | 93.3 | 86.6 | 90.2 | 77.8 | 48.0 | 24.3 |
+| **InCoder-32B** | **32B** | **94.5** | **89.6** | **91.8** | **78.3** | **49.8** | **31.1** |
+### Industrial Code Benchmarks
+| Domain | Benchmark | InCoder-32B | Claude-Sonnet-4.6 | Qwen3.5-397B-A17B |
+|---|---|:---:|:---:|:---:|
+| **Chip Design** | VeriScope Score | 80.7 | **87.7** | 73.1 |
+| **GPU Optim.** | KernelBench L1/L2/L3 | **22.2/36.0/14.0** | 11.1/28.0/2.0 | 4.0/10.0/0.0 |
+| **3D Modeling** | CAD-Coder Compile (%) | **82.0** | 77.0 | 79.0 |
+## Training Pipeline: Code-Flow
+InCoder-32B was developed using a three-stage **Code-Flow** pipeline:
+1. **Pre-training & Annealing:** Curated industrial code mixed with general code pre-training using multi-level deduplication.
+2. **Mid-training (Context Extension):** Progressive context extension from 8K to 128K tokens using synthetic industrial reasoning data and agent trajectories.
+3. **Post-training:** Execution-grounded SFT across hardware design, GPU kernels, and systems programming.
+## Quickstart
+### Installation
+```bash
+pip install -U "transformers>=4.57.1" accelerate safetensors
+```
+### Usage with Transformers
+Note: The model uses a custom architecture, so `trust_remote_code=True` is required.
+```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))
+```
+## Citation
+```bibtex
+@article{yang2026incoder,
+  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={2026}
+}
+```
+## 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.