Instruct_GPT/README.md

language, license, tags, datasets, pipeline_tag, model-index

language	license	tags	datasets	pipeline_tag	model-index
en	mit	gpt2-medium	databricks/databricks-dolly-15k	text-generation	name results Instruct_GPT task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 28.24 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/Instruct_GPT Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 39.33 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/Instruct_GPT Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 26.84 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/Instruct_GPT Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 39.72 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/Instruct_GPT Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 54.3 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/Instruct_GPT Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 0.3 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/Instruct_GPT Open LLM Leaderboard

This model is a finetuned version of gpt2-medium using databricks/databricks-dolly-15k dataset

Model description

GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.

More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifting one token (word or piece of word) to the right. The model uses a mask mechanism to make sure the predictions for the token i only use the inputs from 1 to i but not the future tokens.

This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a prompt.

To use this model

>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model_name = "Sharathhebbar24/Instruct_GPT"
>>> model = AutoModelForCausalLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2-medium")
>>> def generate_text(prompt):
>>>  inputs = tokenizer.encode(prompt, return_tensors='pt')
>>>  outputs = mod1.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
>>>  generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>>  return generated[:generated.rfind(".")+1]

>>> generate_text("Should I Invest in stocks")

Should I Invest in stocks? Investing in stocks is a great way to diversify your portfolio.  You can invest in stocks based on the market's performance, or you can invest in stocks based on the company's performance.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	31.46
AI2 Reasoning Challenge (25-Shot)	28.24
HellaSwag (10-Shot)	39.33
MMLU (5-Shot)	26.84
TruthfulQA (0-shot)	39.72
Winogrande (5-shot)	54.30
GSM8k (5-shot)	0.30