Update README.md

README.md

@@ -4,7 +4,7 @@ license: gpl-3.0
 
 # Description
 
-This model demonstrates that GPT-J can work perfectly well as an "instruct" model when properly fine-tuned. 
+This model demonstrates that GPT-J can work perfectly well as an "instruct" model when properly fine-tuned. It is an fp16 version that makes it easy to deploy the model an entry level GPU like an NVIDIA Tesla T4. Want to know more about NLP Cloud? [Have a look at our platform here](https://nlpcloud.com).
 
 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).
 
@@ -26,7 +26,7 @@ I do not wan to go
 Correction:
 ```
 
-Now, with Instruct GPT-J, here is what you can do:
+Now, with Instruct GPT-J, you can ask things in natural language "like a human":
 
 ```text
 Correct spelling and grammar from the following text.
@@ -39,9 +39,11 @@ Which returns the following:
 I do not want to go.
 ```
 
+You can also perfectly keep using few-shot learning on this model for very advanced use cases.
+
 ## How To Use The Model?
 
-Using the model in FP16 with the text generation pipeline, here is what you can do:
+Using the model in fp16 with the text generation pipeline, here is what you can do:
 
 ```python
 from transformers import pipeline
@@ -54,7 +56,7 @@ prompt = "Correct spelling and grammar from the following text.\nI do not wan to
 print(generator(prompt))
 ```
 
-You can also use the `generate()` function, here is what you can do:
+You can also use the `generate()` function. Here is what you can do:
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -69,4 +71,11 @@ inputs = tokenizer(prompt, return_tensors='pt')
 outputs = generator.generate(inputs.input_ids.cuda())
 
 print(tokenizer.decode(outputs[0]))
-```
+```
+
+## Hardware Requirements
+
+This model is an fp16 version of our fine-tuned model, which works very well on a GPU with 16GB of VRAM like an NVIDIA Tesla T4.
+
+We did not notice any difference between the fp32 and fp16 versions in terms of quality.
+