Instructions to use inverse-scaling/opt-30b_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inverse-scaling/opt-30b_eval with Transformers:

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

pipe = pipeline("text-generation", model="inverse-scaling/opt-30b_eval")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inverse-scaling/opt-30b_eval")
model = AutoModelForCausalLM.from_pretrained("inverse-scaling/opt-30b_eval")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inverse-scaling/opt-30b_eval with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inverse-scaling/opt-30b_eval"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inverse-scaling/opt-30b_eval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/inverse-scaling/opt-30b_eval

SGLang

How to use inverse-scaling/opt-30b_eval 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 "inverse-scaling/opt-30b_eval" \
    --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": "inverse-scaling/opt-30b_eval",
		"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 "inverse-scaling/opt-30b_eval" \
        --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": "inverse-scaling/opt-30b_eval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use inverse-scaling/opt-30b_eval with Docker Model Runner:
```
docker model run hf.co/inverse-scaling/opt-30b_eval
```

Add evaluation results on the mathemakitten--winobias_antistereotype_test_cot_v4 config and test split of mathemakitten/winobias_antistereotype_test_cot_v4

#13

by autoevaluator HF Staff - opened Nov 7, 2022

base: refs/heads/main

←

from: refs/pr/13

Discussion Files changed

+17

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autoevaluator

Nov 7, 2022

Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!
Your model has been evaluated on the mathemakitten--winobias_antistereotype_test_cot_v4 config and test split of the mathemakitten/winobias_antistereotype_test_cot_v4 dataset by @mathemakitten , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.

Add evaluation results on the mathemakitten--winobias_antistereotype_test_cot_v4 config and test split of mathemakitten/winobias_antistereotype_test_cot_v42313cedb

MicPie changed pull request status to closed Jan 2, 2023

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