Create README.md

README.md

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+---
+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](https://huggingface.co/vikp/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.`.