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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

thesunday commited on Jan 25, 2024

Commit

ed5d8eb

verified ·

1 Parent(s): e35b651

Add base model metadata to model card

Browse files

It would be nice to trace the genealogy of merged models like this: https://colab.research.google.com/drive/1s2eQlolcI1VGgDhqWIANfkfKvcKrMyNr?usp=sharing

Files changed (1) hide show

README.md +3 -0

README.md CHANGED Viewed

@@ -8,6 +8,9 @@ pipeline_tag: text-generation
 tags:
 - Math
 - merge
 ---
 ## MetaMath-Cybertron-Starling

 tags:
 - Math
 - merge
+base_model:
+- Q-bert/MetaMath-Cybertron
+- berkeley-nest/Starling-LM-7B-alpha
 ---
 ## MetaMath-Cybertron-Starling