Quantization made by Richard Erkhov.
We use state-of-the-art
Language Model Evaluation Harness to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.
1# Use a pipeline as a high-level helper
2>>> from transformers import pipeline
3>>> pipe = pipeline("text-generation", model="lgaalves/gpt2_open-platypus")
4>>> question = "What is a large language model?"
5>>> answer = pipe(question)
6>>> print(answer[0]['generated_text'])
1# Load model directly
2from transformers import AutoTokenizer, AutoModelForCausalLM
3
4tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2_open-platypus")
5model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2_open-platypus")
lgaalves/gpt2_open-platypus trained using STEM and logic based dataset
garage-bAInd/Open-Platypus.
You can use the raw model for text generation or fine-tune it to a downstream task. The model was not extensively tested and may produce false information. It contains a lot of unfiltered content from the internet, which is far from neutral.
Detailed results can be found
here
