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

Libraries

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

which toolcalling format for opencode?

by dfsafdsf - opened Apr 10

Discussion

dfsafdsf

Apr 10

i runned your model on llama.cpp with --jinja option, also tryed --chat-template chatml
opencode get
<function=bash>
<parameter=command>ls -la
<parameter=description>List directory contents

and not parse as toolcall

zwpride

Multilingual-Multimodal-NLP org Apr 14

Hi, you can try setting --tool-call-parser qwen3_xml. This should work for the tool call parsing format used by this model.

Example:

vllm serve /path/to/your/model \
    --port 8080 \
    --tensor-parallel-size 1 \
    --data-parallel-size 8 \
    --served-model-name InCoder-32B \
    --disable-log-requests \
    --max-model-len 131072 \
    --gpu-memory-utilization 0.9 \
    --trust-remote-code \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_xml

dfsafdsf

Apr 14

@zwpride i need on llama.cpp)

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