Text Generation
Transformers
PyTorch
TensorFlow
JAX
English
opt
Eval Results (legacy)
text-generation-inference
Instructions to use inverse-scaling/opt-2.7b_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inverse-scaling/opt-2.7b_eval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inverse-scaling/opt-2.7b_eval")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("inverse-scaling/opt-2.7b_eval") model = AutoModelForCausalLM.from_pretrained("inverse-scaling/opt-2.7b_eval") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inverse-scaling/opt-2.7b_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-2.7b_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-2.7b_eval", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/inverse-scaling/opt-2.7b_eval
- SGLang
How to use inverse-scaling/opt-2.7b_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-2.7b_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-2.7b_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-2.7b_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-2.7b_eval", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use inverse-scaling/opt-2.7b_eval with Docker Model Runner:
docker model run hf.co/inverse-scaling/opt-2.7b_eval
Commit History
Add evaluation results on the mathemakitten--winobias_antistereotype_test_v5 config and test split of mathemakitten/winobias_antistereotype_test_v5 (#18) 7efae61
Add evaluation results on the mathemakitten--winobias_antistereotype_test_v5 config and test split of mathemakitten/winobias_antistereotype_test_v5 (#17) d4491fe
Add evaluation results on the mathemakitten--winobias_antistereotype_test_v5 config and test split of mathemakitten/winobias_antistereotype_test_v5 (#16) 44a34de
Add evaluation results on the mathemakitten--winobias_antistereotype_test_cot_v3 config and test split of mathemakitten/winobias_antistereotype_test_cot_v3 (#13) 7e07887
Add evaluation results on the mathemakitten--winobias_antistereotype_test_cot_v1 config and test split of mathemakitten/winobias_antistereotype_test_cot_v1 (#12) f454423
Add evaluation results on the inverse-scaling--hindsight-neglect-10shot config and train split of inverse-scaling/hindsight-neglect-10shot (#6) 99c9b7f
Update README.md f031bd2
Update README.md eaa0bb2
Add evaluation results on the inverse-scaling--NeQA config and train split of inverse-scaling/NeQA (#3) 92250f6
Update README.md bdc8ddd
Add evaluation results on the inverse-scaling--41 config and train split of inverse-scaling/41 (#2) 7e749b5
cp opt-2.7b 2a83fcf
Michael Pieler commited on