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	---
	license: gpl-3.0
	datasets:
	- nlpcloud/instructions-dataset-adapted-from-stanford-alpaca-for-gpt-j
	widget:
	- example_title: "Spelling Correction"
	text: Correct spelling and grammar from the following text.\nI do not wan to go\n
	- example_title: "Story Generation"
	text: Write a short story about space.\n
	---

	# Description

	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 on 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).

	The dataset was slightly reworked in order to match the GPT-J fine-tuning format with [Mesh Transformer Jax](https://github.com/kingoflolz/mesh-transformer-jax) on TPUs. [Here is the final dataset we used](https://huggingface.co/datasets/nlpcloud/instructions-dataset-adapted-from-stanford-alpaca-for-gpt-j).

	The base GPT-J model needs few-shot learning in order to properly understand what you want. [See more details here about how to properly use few-shot learning](https://nlpcloud.com/effectively-using-gpt-j-gpt-neo-gpt-3-alternatives-few-shot-learning.html). For example let's say that you want to correct spelling with GPT-J. Here is an example of a prompt you had to use:

	```text
	I love goin to the beach.
	Correction: I love going to the beach.
	###
	Let me hav it!
	Correction: Let me have it!
	###
	It have too many drawbacks.
	Correction: It has too many drawbacks.
	###
	I do not wan to go
	Correction:
	```

	Now, with Instruct GPT-J, you can ask things in natural language "like a human":

	```text
	Correct spelling and grammar from the following text.
	I do not wan to go\n
	```

	Which returns the following:

	```text
	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:

	```python
	from transformers import pipeline
	import torch

	generator = pipeline(model="nlpcloud/instruct-gpt-j-fp16", torch_dtype=torch.float16, device=0)

	prompt = "Correct spelling and grammar from the following text.\nI do not wan to go\n"

	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-fp16')
	generator = AutoModelForCausalLM.from_pretrained("nlpcloud/instruct-gpt-j-fp16",torch_dtype=torch.float16).cuda()

	prompt = "Correct spelling and grammar from the following text.\nI do not wan to go\n"

	inputs = tokenizer(prompt, return_tensors='pt')
	outputs = generator.generate(inputs.input_ids.cuda())

	print(tokenizer.decode(outputs[0]))
	```

	## Special Note About Input Format

	Due to the way this model was fine-tuned, you should always use new lines at the end of your instructions.

	For example the following instruction might not always work:

	```text
	Correct spelling and grammar from the following text.\nI do not wan to go
	```

	But this one would:

	```text
	Correct spelling and grammar from the following text.\nI do not wan to go\n
	```

	## 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.