Instructions to use inverse-scaling/opt-13b_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inverse-scaling/opt-13b_eval with Transformers:

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

pipe = pipeline("text-generation", model="inverse-scaling/opt-13b_eval")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inverse-scaling/opt-13b_eval")
model = AutoModelForCausalLM.from_pretrained("inverse-scaling/opt-13b_eval")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inverse-scaling/opt-13b_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-13b_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-13b_eval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/inverse-scaling/opt-13b_eval

SGLang

How to use inverse-scaling/opt-13b_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-13b_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-13b_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-13b_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-13b_eval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use inverse-scaling/opt-13b_eval with Docker Model Runner:
```
docker model run hf.co/inverse-scaling/opt-13b_eval
```

Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator

#17

by autoevaluator HF Staff - opened Dec 5, 2022

base: refs/heads/main

←

from: refs/pr/17

Discussion Files changed

+44

-30

autoevaluator

Dec 5, 2022

Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋! We've added a new verifyToken field to your evaluation results to verify that they are produced by the model evaluator. Accept this PR to ensure that your results remain listed as verified on the Hub leaderboard.

Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluatorfdc3b93a

MicPie changed pull request status to merged Jan 2, 2023

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