Instructions to use benjamin/Gemma2-2B-Distilled-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use benjamin/Gemma2-2B-Distilled-Math with Transformers:

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

pipe = pipeline("text-generation", model="benjamin/Gemma2-2B-Distilled-Math")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("benjamin/Gemma2-2B-Distilled-Math")
model = AutoModelForCausalLM.from_pretrained("benjamin/Gemma2-2B-Distilled-Math", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use benjamin/Gemma2-2B-Distilled-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "benjamin/Gemma2-2B-Distilled-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "benjamin/Gemma2-2B-Distilled-Math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/benjamin/Gemma2-2B-Distilled-Math

SGLang

How to use benjamin/Gemma2-2B-Distilled-Math 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 "benjamin/Gemma2-2B-Distilled-Math" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "benjamin/Gemma2-2B-Distilled-Math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "benjamin/Gemma2-2B-Distilled-Math" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "benjamin/Gemma2-2B-Distilled-Math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use benjamin/Gemma2-2B-Distilled-Math with Docker Model Runner:
```
docker model run hf.co/benjamin/Gemma2-2B-Distilled-Math
```

Gemma2-2B-Distilled-Math / README.md

benjamin

Update README.md

66a3e78 verified over 1 year ago

preview code

Raw

History Blame Contribute Delete

1.72 kB

	---
	library_name: transformers
	datasets:
	- nvidia/OpenMathInstruct-2
	base_model:
	- google/gemma-2-2b-it
	---

	# Gemma2-2B Distilled from OpenMath2-Llama3.1-8B Model Card

	Gemma2-2B distilled from [OpenMath2-Llama3.1-8B](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B) for math tasks.

	This model greatly outperforms the general-purpose Gemma2 instruction-tuning finetune on math tasks.

	## Model Details

	- Base Model: Gemma2-2B
	- Tokenization: Gemma2-2B
	- Training Methodology: Distillation from OpenMath2-Llama3.1-8B on [OpenMathInstruct-2](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2).


	\| Benchmark \| Gemma2-2B-Distilled-Math \| Original Gemma2-2B-IT \|
	\|---------------\|------------------------------------\|------------------------\|
	\| GSM8K (zero-shot) \| 65.1 \| 6.1 \|
	\| MATH (zero-shot) \| 52.1 \| 9.9 \|



	## Model Details

	Details on the training methodology are forthcoming.

	## Use

	```python
	import torch
	from transformers import pipeline

	template = "<\|start_header_id\|>user<\|end_header_id\|>\n\nSolve the following math problem. Make sure to put the answer (and only answer) inside \boxed{}.\n\n{{problem}}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>\n\n"
	problem = "What is the minimum value of $a^2+6a-7$?"

	pipe = pipeline(
	"text-generation",
	model="benjamin/Gemma2-2B-Distilled-Math",
	model_kwargs={"torch_dtype": torch.bfloat16},
	eos_token_id=107,
	device_map="auto",
	)


	outputs = pipe(template.format(problem), max_new_tokens=256)
	assistant_response = outputs[0]["generated_text"].strip()
	print(assistant_response)
	```