Instructions to use sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP with Transformers:

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

pipe = pipeline("text-generation", model="sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP")
model = AutoModelForCausalLM.from_pretrained("sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP", 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 sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP

SGLang

How to use sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP 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 "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP" \
    --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": "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP",
		"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 "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP" \
        --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": "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP with Docker Model Runner:
```
docker model run hf.co/sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP
```

OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP / model-00002-of-00002.safetensors

Xuandong

Upload folder using huggingface_hub

3afd722 verified about 1 year ago

Download

History Blame

4.65 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: f21e3ed6d5aa72f1e0f3a164755f6e6ae2763673ed80d99fd4d2af41ca398e6c
Size of remote file:: 4.65 GB
SHA256:: 418edc9795b55630083ae33aa9476ea2b893cfb24831fde41bb5a6667f173a45

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.