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
```

Update README.md

by zwpride-iquestlab - opened Mar 26

base: refs/heads/main

←

from: refs/pr/2

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README.md CHANGED Viewed

@@ -137,6 +137,13 @@ outputs = model.generate(
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 ### Fill-in-the-Middle (FIM)
 InCoder-32B supports FIM completion for code infilling tasks:

 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
+### Deployment with vLLM
+For production deployment, you can use vLLM to create an OpenAI-compatible API endpoint.
+```
+vllm serve Multilingual-Multimodal-NLP/IndustrialCoder --tensor-parallel-size 8
+```
 ### Fill-in-the-Middle (FIM)
 InCoder-32B supports FIM completion for code infilling tasks: