metadata
license: cc
datasets:
- vikp/reverse_instruct
This model will generate instructions given some text. It is useful for labelling unlabeled datasets. It's based on a llama 7B model with 32k context length (togethercomputer/LLaMA-2-7B-32K).
It was trained across the reverse-instruct dataset for 2 epochs. Final validation loss was .72, with rouge-l of .66 .
Here is an inference example:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("vikp/reverse_instruct")
tokenizer = AutoTokenizer.from_pretrained("vikp/reverse_instruct")
prompt = """
Output
int i,j; for (i=0;i<numbers.size();i++) for (j=i+1;j<numbers.size();j++) if (abs(numbers[i]-numbers[j])<threshold) return true; return false; }
======
Instruction
""".strip()
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(texts)
And the output instruction for the above example would be Write a C++ program to find the closest pair of numbers in an array..