Instructions to use Q-bert/MetaMath-Cybertron-Starling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Q-bert/MetaMath-Cybertron-Starling with Transformers:

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

pipe = pipeline("text-generation", model="Q-bert/MetaMath-Cybertron-Starling")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Q-bert/MetaMath-Cybertron-Starling")
model = AutoModelForCausalLM.from_pretrained("Q-bert/MetaMath-Cybertron-Starling", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Q-bert/MetaMath-Cybertron-Starling with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Q-bert/MetaMath-Cybertron-Starling

SGLang

How to use Q-bert/MetaMath-Cybertron-Starling 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 "Q-bert/MetaMath-Cybertron-Starling" \
    --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": "Q-bert/MetaMath-Cybertron-Starling",
		"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 "Q-bert/MetaMath-Cybertron-Starling" \
        --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": "Q-bert/MetaMath-Cybertron-Starling",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Q-bert/MetaMath-Cybertron-Starling with Docker Model Runner:
```
docker model run hf.co/Q-bert/MetaMath-Cybertron-Starling
```

MetaMath-Cybertron-Starling / README.md

Q-bert

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	---
	license: cc-by-nc-4.0
	datasets:
	- meta-math/MetaMathQA
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- Math
	- merge
	base_model:
	- Q-bert/MetaMath-Cybertron
	- berkeley-nest/Starling-LM-7B-alpha
	---

	## MetaMath-Cybertron-Starling


	Merge [Q-bert/MetaMath-Cybertron](https://huggingface.co/Q-bert/MetaMath-Cybertron) and [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) using slerp merge.

	You can use ChatML format.

	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [Here](https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/Q-bert/MetaMath-Cybertron-Starling/results_2023-12-07T21-59-56.458563.json)

	\| Metric \| Value \|
	\|-----------------------\|---------------------------\|
	\| Avg. \| 71.35 \|
	\| ARC (25-shot) \| 67.75 \|
	\| HellaSwag (10-shot) \| 86.23 \|
	\| MMLU (5-shot) \| 65.24 \|
	\| TruthfulQA (0-shot) \| 55.94 \|
	\| Winogrande (5-shot) \| 81.45 \|
	\| GSM8K (5-shot) \| 71.49 \|