Instructions to use jaehyeokdoo2/SweRankLLMMulti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaehyeokdoo2/SweRankLLMMulti with Transformers:

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

pipe = pipeline("text-generation", model="jaehyeokdoo2/SweRankLLMMulti")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaehyeokdoo2/SweRankLLMMulti")
model = AutoModelForCausalLM.from_pretrained("jaehyeokdoo2/SweRankLLMMulti")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jaehyeokdoo2/SweRankLLMMulti with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaehyeokdoo2/SweRankLLMMulti"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaehyeokdoo2/SweRankLLMMulti",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaehyeokdoo2/SweRankLLMMulti

SGLang

How to use jaehyeokdoo2/SweRankLLMMulti 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 "jaehyeokdoo2/SweRankLLMMulti" \
    --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": "jaehyeokdoo2/SweRankLLMMulti",
		"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 "jaehyeokdoo2/SweRankLLMMulti" \
        --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": "jaehyeokdoo2/SweRankLLMMulti",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaehyeokdoo2/SweRankLLMMulti with Docker Model Runner:
```
docker model run hf.co/jaehyeokdoo2/SweRankLLMMulti
```

SweRankLLMMulti

Commit History

Upload Qwen2ForCausalLM

034f4fc
verified

JaeHyeok Doo commited on Sep 23, 2025

Upload tokenizer

1568569
verified

JaeHyeok Doo commited on Sep 23, 2025

initial commit

c24ad78
verified

JaeHyeok Doo commited on Sep 23, 2025

Commit History

Upload Qwen2ForCausalLM 034f4fc verified

Upload tokenizer 1568569 verified

initial commit c24ad78 verified

Upload Qwen2ForCausalLM

034f4fc
verified

Upload tokenizer

1568569
verified

initial commit

c24ad78
verified