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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Multilingual-Multimodal-NLP/LoopCoder-V2", trust_remote_code=True, device_map="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
```

LoopCoder-V2 / .eval_results /swe-bench_verified.yaml

csjiaya

nielsr HF Staff

Add community evaluation results for Terminal-Bench 2.0, SWE-bench Verified (#7)

5ce3d4f 3 days ago

Raw History Blame Contribute Delete

200 Bytes

- dataset:
    id: SWE-bench/SWE-bench_Verified
    task_id: swe_bench_%_resolved
  value: 64.4
  source:
    url: https://huggingface.co/Multilingual-Multimodal-NLP/LoopCoder-V2
    name: Model Card

	- dataset:
	id: SWE-bench/SWE-bench_Verified
	task_id: swe_bench_%_resolved
	value: 64.4
	source:
	url: https://huggingface.co/Multilingual-Multimodal-NLP/LoopCoder-V2
	name: Model Card