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
```

zwpride commited on 4 days ago

Commit

b87cf3a

verified ·

1 Parent(s): c18e85b

Update README.md

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README.md CHANGED Viewed

@@ -76,42 +76,6 @@ vllm serve $MODEL --port 8080 \
     --cudagraph-capture-sizes 1 2 4 8 12 16 24 32
 ```
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-repo_id = "Multilingual-Multimodal-NLP/LoopCoder-V2"
-tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(
-    repo_id,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    trust_remote_code=True,
-)
-messages = [
-    {"role": "user", "content": "Write a Python function that checks whether a string is a palindrome."}
-]
-inputs = tokenizer.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_tensors="pt",
-).to(model.device)
-outputs = model.generate(
-    inputs,
-    max_new_tokens=512,
-    do_sample=True,
-    temperature=0.6,
-    top_p=0.95,
-    top_k=20,
-)
-print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
-```
 ## Training Data

     --cudagraph-capture-sizes 1 2 4 8 12 16 24 32
 ```
 ## Training Data