Instructions to use hyunseoki/verl-math-transfer-7bi-to-7bi-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hyunseoki/verl-math-transfer-7bi-to-7bi-v2 with Transformers:

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

pipe = pipeline("text-generation", model="hyunseoki/verl-math-transfer-7bi-to-7bi-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hyunseoki/verl-math-transfer-7bi-to-7bi-v2")
model = AutoModelForCausalLM.from_pretrained("hyunseoki/verl-math-transfer-7bi-to-7bi-v2", 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 hyunseoki/verl-math-transfer-7bi-to-7bi-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hyunseoki/verl-math-transfer-7bi-to-7bi-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": "hyunseoki/verl-math-transfer-7bi-to-7bi-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hyunseoki/verl-math-transfer-7bi-to-7bi-v2

SGLang

How to use hyunseoki/verl-math-transfer-7bi-to-7bi-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 "hyunseoki/verl-math-transfer-7bi-to-7bi-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": "hyunseoki/verl-math-transfer-7bi-to-7bi-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 "hyunseoki/verl-math-transfer-7bi-to-7bi-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": "hyunseoki/verl-math-transfer-7bi-to-7bi-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hyunseoki/verl-math-transfer-7bi-to-7bi-v2 with Docker Model Runner:
```
docker model run hf.co/hyunseoki/verl-math-transfer-7bi-to-7bi-v2
```

VERL Math Transfer 7B to 7B v2

Math transfer experiment trained with verl. This repo groups all exported Hugging Face checkpoints for the 7B-to-7B v2 configuration.

Layout

main: latest exported checkpoint, currently step-150
step revisions: step-010, step-020, step-030, step-040, step-050, step-060, step-070, step-080, step-090, step-100, step-110, step-120, step-130, step-140, step-150

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "hyunseoki/verl-math-transfer-7bi-to-7bi-v2"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)

Load a specific checkpoint revision:

from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "hyunseoki/verl-math-transfer-7bi-to-7bi-v2"
revision = "step-150"
tokenizer = AutoTokenizer.from_pretrained(repo_id, revision=revision, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, revision=revision, trust_remote_code=True)

Notes

Architecture detected from the exported config: Qwen2ForCausalLM
The original base model Hub ID is not encoded in these local checkpoints, so base_model metadata is not set automatically.
Checkpoints were exported from verl FSDP shards into Hugging Face safetensors format.

Downloads last month: 18

Safetensors

Model size

8B params

Tensor type

BF16