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
```

Q-bert commited on Dec 9, 2023

Commit

17c8d4c

1 Parent(s): 7786cea

Update README.md

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@@ -17,14 +17,14 @@ Merge [Q-bert/MetaMath-Cybertron](https://huggingface.co/Q-bert/MetaMath-Cybertr
 You can use ChatML format.
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-Detailed results can be found [Coming soon]()
 | Metric                | Value                     |
 |-----------------------|---------------------------|
-| Avg.                  | Coming soon               |
-| ARC (25-shot)         | Coming soon               |
-| HellaSwag (10-shot)   | Coming soon               |
-| MMLU (5-shot)         | Coming soon               |
-| TruthfulQA (0-shot)   | Coming soon               |
-| Winogrande (5-shot)   | Coming soon               |
-| GSM8K (5-shot)        | Coming soon               |

 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               |