Update README.md

README.md

@@ -2,6 +2,8 @@
 license: gpl-3.0
 ---
 
+# Description
+
 This model demonstrates that GPT-J can work perfectly well as an "instruct" model when properly fine-tuned. 
 
 We fine-tuned GPT-J on an instruction dataset created by the [Stanford Alpaca team](https://github.com/tatsu-lab/stanford_alpaca). You can find the original dataset [here](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json).
@@ -31,3 +33,40 @@ Correct spelling and grammar from the following text.
 I do not wan to go
 ```
 
+Which returns the following:
+
+```text
+I do not want to go.
+```
+
+## How To Use The Model?
+
+Using the model in FP16 with the text generation pipeline, here is what you can do:
+
+```python
+from transformers import pipeline
+import torch
+
+generator = pipeline(model="nlpcloud/instruct-gpt-j", torch_dtype=torch.float16, device=0)
+
+prompt = "Correct spelling and grammar from the following text.\nI do not wan to go"
+
+print(generator(prompt))
+```
+
+You can also use the `generate()` function, here is what you can do:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained('nlpcloud/instruct-gpt-j')
+generator = AutoModelForCausalLM.from_pretrained("nlpcloud/instruct-gpt-j",torch_dtype=torch.float16).cuda()
+
+prompt = "Correct spelling and grammar from the following text.\nI do not wan to go"
+
+inputs = tokenizer(prompt, return_tensors='pt')
+outputs = generator.generate(inputs.input_ids.cuda())
+
+print(tokenizer.decode(outputs[0]))
+```