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
```

transformers Err

by cse2011 - opened 2 days ago

Discussion

cse2011

2 days ago

i am getting this err , any clue how to fix

model = AutoModelForCausalLM.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\yyass.conda\envs\bp\Lib\site-packages\transformers\models\auto\auto_factory.py", line 409, in from_pretrained
raise ValueError(

model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
    quantization_config=bnb_config,
    trust_remote_code=True
)

ValueError: Unrecognized configuration class <class 'transformers_modules.looped_coder.651add1469072b0a.configuration_iquestpltcoder.IQuestPLTCoderConfig'> for this kind of AutoModel: AutoModelForCausalLM.
Model type should be one of GPT2Config, AfmoeConfig, ApertusConfig, ArceeConfig, AriaTextConfig, BambaConfig, BartConfig, BertConfig, BertGenerationConfig, BigBirdConfig, BigBirdPegasusConfig, BioGptConfig, BitNetConfig, BlenderbotConfig, BlenderbotSmallConfig, BloomConfig, BltConfig, CamembertConfig, CodeGenConfig, CohereConfig, Cohere2Config, Cohere2MoeConfig, CpmAntConfig, CTRLConfig, CwmConfig, Data2VecTextConfig, DbrxConfig, DeepseekV2Config, DeepseekV3Config, DeepseekV32Config, DeepseekV4Config, DiffLlamaConfig, DogeConfig, Dots1Config, ElectraConfig, Emu3Config, ErnieConfig, Ernie4_5Config, Ernie4_5_MoeConfig, Exaone4Config, ExaoneMoeConfig, FalconConfig, FalconH1Config, FalconMambaConfig, FlexOlmoConfig, FuyuConfig, GemmaConfig, Gemma2Config, Gemma3Config, Gemma3TextConfig, Gemma3nConfig, Gemma3nTextConfig, Gemma4Config, Gemma4AssistantConfig, Gemma4TextConfig, Gemma4UnifiedConfig, Gemma4UnifiedAssistantConfig, Gemma4UnifiedTextConfig, GitConfig, GlmConfig, Glm4Config, Glm4MoeConfig, Glm4MoeLiteConfig, GlmMoeDsaConfig, GotOcr2Config, GPT2Config, GPTBigCodeConfig, GPTNeoConfig, GPTNeoXConfig, GPTNeoXJapaneseConfig, GptOssConfig, GPTJConfig, GraniteConfig, GraniteMoeConfig, GraniteMoeHybridConfig, GraniteMoeSharedConfig, HeliumConfig, HrmTextConfig, HunYuanDenseV1Config, HunYuanMoEV1Config, HYV3Config, HyperCLOVAXConfig, Jais2Config, JambaConfig, JetMoeConfig, LagunaConfig, Lfm2Config, Lfm2MoeConfig, LlamaConfig, Llama4Config, Llama4TextConfig, LongcatFlashConfig, MambaConfig, Mamba2Config, MarianConfig, MBartConfig, MegatronBertConfig, MellumConfig, MiniMaxConfig, MiniMaxM2Config, MiniMaxM3VLTextConfig, MinistralConfig, Ministral3Config, MistralConfig, MixtralConfig, MllamaConfig, ModernBertDecoderConfig, MoshiConfig, MptConfig, MusicgenConfig, MusicgenMelodyConfig, MvpConfig, NanoChatConfig, NemotronConfig, NemotronHConfig, OlmoConfig, Olmo2Config, Olmo3Config, OlmoHybridConfig, OlmoeConfig, OpenAIGPTConfig, OPTConfig, PegasusConfig, PersimmonConfig, PhiConfig, Phi3Config, Phi4MultimodalConfig, PhimoeConfig, PLBartConfig, ProphetNetConfig, Qwen2Config, Qwen2MoeConfig, Qwen3Config, Qwen3_5Config, Qwen3_5MoeConfig, Qwen3_5MoeTextConfig, Qwen3_5TextConfig, Qwen3MoeConfig, Qwen3NextConfig, RecurrentGemmaConfig, ReformerConfig, RemBertConfig, RobertaConfig, RobertaPreLayerNormConfig, RoCBertConfig, RoFormerConfig, RwkvConfig, SeedOssConfig, SmolLM3Config, SolarOpenConfig, StableLmConfig, Starcoder2Config, TrOCRConfig, VaultGemmaConfig, WhisperConfig, XGLMConfig, XLMConfig, XLMRobertaConfig, XLMRobertaXLConfig, XLNetConfig, xLSTMConfig, XmodConfig, YoutuConfig, ZambaConfig, Zamba2Config.

zwpride

Multilingual-Multimodal-NLP org about 9 hours ago

Thank you for using our model, and we apologize for any inconvenience caused. We currently recommend using the specified version of vLLM for inference. Please refer to the updated README for usage instructions.

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