Instructions to use Multilingual-Multimodal-NLP/LoopCoder-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multilingual-Multimodal-NLP/LoopCoder-V2 with Transformers:

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

pipe = pipeline("text-generation", model="Multilingual-Multimodal-NLP/LoopCoder-V2", 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/LoopCoder-V2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Multilingual-Multimodal-NLP/LoopCoder-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Multilingual-Multimodal-NLP/LoopCoder-V2"
# 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/LoopCoder-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Multilingual-Multimodal-NLP/LoopCoder-V2

SGLang

How to use Multilingual-Multimodal-NLP/LoopCoder-V2 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/LoopCoder-V2" \
    --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/LoopCoder-V2",
		"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/LoopCoder-V2" \
        --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/LoopCoder-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Multilingual-Multimodal-NLP/LoopCoder-V2 with Docker Model Runner:
```
docker model run hf.co/Multilingual-Multimodal-NLP/LoopCoder-V2
```

Bug: Missing modeling_iquestpltcoder.py file prevents model from loading

by JohnMolotov - opened about 23 hours ago

Discussion

JohnMolotov

about 23 hours ago

Hi there,

Thank you for releasing the LoopCoder-V2 model!

It looks like the repository is currently missing the core modeling file required to initialize the custom architecture. While configuration_iquestpltcoder.py and tokenization_iquestcoder.py are present, modeling_iquestpltcoder.py appears to have been omitted during the upload.

When attempting to load the model via standard HF transformers or vLLM with trust_remote_code=True, it fails at architecture resolution because the AutoModelForCausalLM hook has no blueprint to construct the layer weights.

Could you please upload modeling_iquestpltcoder.py to the repository? Thank you!

devo111

about 22 hours ago

I ran into the same problem. There's a YouTube video where the model is being tested, but I think he's running the model in V1, not V2. https://www.youtube.com/watch?v=ruxvS5Bd3mU

csjiaya

Multilingual-Multimodal-NLP org about 22 hours ago

Sry for inconvenience, we will release the codebase for inference of v2 version

csjiaya

Multilingual-Multimodal-NLP org about 2 hours ago

Hi, we updated the readme, and users can use the vLLM branch for inference.

For vLLM inference, install vLLM from yxing-bj/vllm and use transformers==4.57.1, then start the server with the following command:

vllm serve $MODEL --port 8080 \
    --max-num-batched-tokens 8192 --max-num-seqs 512 -tp 1 -dp 1 --trust-remote-code \
    --cudagraph-capture-sizes 1 2 4 8 12 16 24 32

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