Instructions to use Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8 with Transformers:

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

pipe = pipeline("text-generation", model="Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8")
model = AutoModelForCausalLM.from_pretrained("Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8", 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 Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8

SGLang

How to use Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8 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 "Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8" \
    --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": "Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8",
		"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 "Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8" \
        --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": "Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8 with Docker Model Runner:
```
docker model run hf.co/Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8
```

tiny-think-dpo-math-stem-dpo-beta1-lr3e-6-e1-bs8 / model.safetensors

Shekswess

Training in progress, step 358

ab1f3a5 verified 7 months ago

Download

History Blame Contribute Delete

281 MB

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:: 075281b3d9a6771ac8c2f6f2938205226eed3d36814b715b4b2c8e468158a758
Size of remote file:: 281 MB
SHA256:: 7d46ea5ca81637526a5d5734c3f92d7d0380dff308165a1163be29e02e6e0bbb

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