Instructions to use neginr/OpenR1-Math-Raw-all-correct-5k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neginr/OpenR1-Math-Raw-all-correct-5k with Transformers:

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

pipe = pipeline("text-generation", model="neginr/OpenR1-Math-Raw-all-correct-5k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neginr/OpenR1-Math-Raw-all-correct-5k")
model = AutoModelForCausalLM.from_pretrained("neginr/OpenR1-Math-Raw-all-correct-5k")
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

vLLM

How to use neginr/OpenR1-Math-Raw-all-correct-5k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neginr/OpenR1-Math-Raw-all-correct-5k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neginr/OpenR1-Math-Raw-all-correct-5k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neginr/OpenR1-Math-Raw-all-correct-5k

SGLang

How to use neginr/OpenR1-Math-Raw-all-correct-5k 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 "neginr/OpenR1-Math-Raw-all-correct-5k" \
    --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": "neginr/OpenR1-Math-Raw-all-correct-5k",
		"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 "neginr/OpenR1-Math-Raw-all-correct-5k" \
        --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": "neginr/OpenR1-Math-Raw-all-correct-5k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use neginr/OpenR1-Math-Raw-all-correct-5k with Docker Model Runner:
```
docker model run hf.co/neginr/OpenR1-Math-Raw-all-correct-5k
```

neginr commited on Apr 28, 2025

Commit

cca1e91

verified ·

1 Parent(s): abdead8

Upload configs.yaml with huggingface_hub

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+assistant_tag: assistant
+bf16: true
+content_tag: value
+cutoff_len: 16384
+dataloader_num_workers: 4
+dataloader_persistent_workers: true
+dataloader_pin_memory: true
+dataset: neginr/OpenR1-Math-Raw-all-correct-5k
+dataset_dir: ONLINE
+ddp_timeout: 180000000
+deepspeed: dcft/train/zero3.json
+do_train: true
+enable_liger_kernel: true
+finetuning_type: full
+formatting: sharegpt
+global_batch_size: 96
+gradient_accumulation_steps: 3
+hub_model_id: neginr/OpenR1-Math-Raw-all-correct-5k
+include_hp: dcft/train/hp_settings/paper/reasoning_small.yaml
+learning_rate: 2.0e-05
+logging_steps: 1
+lr_scheduler_type: cosine
+messages: conversations
+model_name_or_path: Qwen/Qwen2.5-7B-Instruct
+num_train_epochs: 7.0
+output_dir: /scratch/08134/negin/dcft_checkpoints/r1_annotated_5k
+overwrite_cache: true
+per_device_train_batch_size: 1
+plot_loss: true
+preprocessing_num_workers: 16
+push_to_db: true
+push_to_hub: true
+report_to: wandb
+role_tag: from
+run_name: r1_annotated_5k
+save_strategy: epoch
+stage: sft
+template: qwen25
+user_tag: user
+warmup_ratio: 0.1