Text Generation
Transformers
PyTorch
TensorFlow
JAX
English
opt
Eval Results (legacy)
text-generation-inference
Instructions to use inverse-scaling/opt-13b_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Commit History
Add evaluation results on the mathemakitten--winobias_antistereotype_test_v5 config and test split of mathemakitten/winobias_antistereotype_test_v5 (#16) f36b058
Add evaluation results on the mathemakitten--winobias_antistereotype_test_v5 config and test split of mathemakitten/winobias_antistereotype_test_v5 (#15) fcaf6d2
Add evaluation results on the mathemakitten--winobias_antistereotype_test_v5 config and test split of mathemakitten/winobias_antistereotype_test_v5 (#14) 044becf
Add evaluation results on the mathemakitten--winobias_antistereotype_test_cot_v3 config and test split of mathemakitten/winobias_antistereotype_test_cot_v3 (#11) 6129b11
Add evaluation results on the mathemakitten--winobias_antistereotype_test_cot_v1 config and test split of mathemakitten/winobias_antistereotype_test_cot_v1 (#10) 72fe103
Update README.md 997b07e
Add evaluation results on the inverse-scaling--redefine-math config and train split of inverse-scaling/redefine-math (#4) b74d1b8
Update README.md d07b107
Add evaluation results on the inverse-scaling--quote-repetition config and train split of inverse-scaling/quote-repetition (#3) 053dfe5
Add evaluation results on the inverse-scaling--NeQA config and train split of inverse-scaling/NeQA (#2) 214f35b
Add evaluation results on the inverse-scaling--41 config and train split of inverse-scaling/41 (#1) b96cb30
cp opt-13b 9bb52b1
Michael Pieler commited on