Instructions to use jdoo99/SweRankLLM_GRPO_full_g4_step64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jdoo99/SweRankLLM_GRPO_full_g4_step64 with Transformers:

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

pipe = pipeline("text-generation", model="jdoo99/SweRankLLM_GRPO_full_g4_step64")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jdoo99/SweRankLLM_GRPO_full_g4_step64")
model = AutoModelForCausalLM.from_pretrained("jdoo99/SweRankLLM_GRPO_full_g4_step64", device_map="auto")
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 jdoo99/SweRankLLM_GRPO_full_g4_step64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jdoo99/SweRankLLM_GRPO_full_g4_step64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jdoo99/SweRankLLM_GRPO_full_g4_step64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jdoo99/SweRankLLM_GRPO_full_g4_step64

SGLang

How to use jdoo99/SweRankLLM_GRPO_full_g4_step64 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 "jdoo99/SweRankLLM_GRPO_full_g4_step64" \
    --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": "jdoo99/SweRankLLM_GRPO_full_g4_step64",
		"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 "jdoo99/SweRankLLM_GRPO_full_g4_step64" \
        --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": "jdoo99/SweRankLLM_GRPO_full_g4_step64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jdoo99/SweRankLLM_GRPO_full_g4_step64 with Docker Model Runner:
```
docker model run hf.co/jdoo99/SweRankLLM_GRPO_full_g4_step64
```

SweRankLLM_GRPO_full_g4_step64

Commit History

Upload Qwen2ForCausalLM

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JaeHyeok Doo commited on Apr 14, 2025

Upload tokenizer

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JaeHyeok Doo commited on Apr 14, 2025

initial commit

67da87e
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JaeHyeok Doo commited on Apr 14, 2025

Commit History

Upload Qwen2ForCausalLM 79e070f verified

Upload tokenizer 800549c verified

initial commit 67da87e verified

Upload Qwen2ForCausalLM

79e070f
verified

Upload tokenizer

800549c
verified

initial commit

67da87e
verified