Instructions to use jaehyeokdoo2/SweRankLLMMulti-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaehyeokdoo2/SweRankLLMMulti-7B with Transformers:

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

pipe = pipeline("text-generation", model="jaehyeokdoo2/SweRankLLMMulti-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaehyeokdoo2/SweRankLLMMulti-7B")
model = AutoModelForCausalLM.from_pretrained("jaehyeokdoo2/SweRankLLMMulti-7B")
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-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaehyeokdoo2/SweRankLLMMulti-7B"
# 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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaehyeokdoo2/SweRankLLMMulti-7B

SGLang

How to use jaehyeokdoo2/SweRankLLMMulti-7B 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-7B" \
    --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-7B",
		"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-7B" \
        --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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaehyeokdoo2/SweRankLLMMulti-7B with Docker Model Runner:
```
docker model run hf.co/jaehyeokdoo2/SweRankLLMMulti-7B
```

SweRankLLMMulti-7B

Commit History

Upload tokenizer

0084734
verified

JaeHyeok Doo commited on Sep 25, 2025

Upload Qwen2ForCausalLM

e85a254
verified

JaeHyeok Doo commited on Sep 25, 2025

initial commit

0d0ee48
verified

JaeHyeok Doo commited on Sep 25, 2025

Commit History

Upload tokenizer 0084734 verified

Upload Qwen2ForCausalLM e85a254 verified

initial commit 0d0ee48 verified

Upload tokenizer

0084734
verified

Upload Qwen2ForCausalLM

e85a254
verified

initial commit

0d0ee48
verified