103 lines
3.6 KiB
Markdown
103 lines
3.6 KiB
Markdown
---
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license: gpl-3.0
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datasets:
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- nlpcloud/instructions-dataset-adapted-from-stanford-alpaca-for-gpt-j
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widget:
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- example_title: "Spelling Correction"
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text: Correct spelling and grammar from the following text.\nI do not wan to go\n
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- example_title: "Story Generation"
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text: Write a short story about space.\n
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---
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# Description
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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).
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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).
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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).
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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:
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```text
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I love goin to the beach.
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Correction: I love going to the beach.
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###
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Let me hav it!
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Correction: Let me have it!
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###
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It have too many drawbacks.
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Correction: It has too many drawbacks.
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###
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I do not wan to go
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Correction:
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```
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Now, with Instruct GPT-J, you can ask things in natural language "like a human":
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```text
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Correct spelling and grammar from the following text.
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I do not wan to go\n
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```
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Which returns the following:
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```text
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I do not want to go.
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```
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You can also perfectly keep using few-shot learning on this model for very advanced use cases.
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## How To Use The Model?
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Using the model in fp16 with the text generation pipeline, here is what you can do:
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```python
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from transformers import pipeline
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import torch
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generator = pipeline(model="nlpcloud/instruct-gpt-j-fp16", torch_dtype=torch.float16, device=0)
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prompt = "Correct spelling and grammar from the following text.\nI do not wan to go\n"
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print(generator(prompt))
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```
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You can also use the `generate()` function. Here is what you can do:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained('nlpcloud/instruct-gpt-j-fp16')
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generator = AutoModelForCausalLM.from_pretrained("nlpcloud/instruct-gpt-j-fp16",torch_dtype=torch.float16).cuda()
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prompt = "Correct spelling and grammar from the following text.\nI do not wan to go\n"
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inputs = tokenizer(prompt, return_tensors='pt')
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outputs = generator.generate(inputs.input_ids.cuda())
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print(tokenizer.decode(outputs[0]))
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```
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## Special Note About Input Format
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Due to the way this model was fine-tuned, you should always use new lines at the end of your instructions.
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For example the following instruction might not always work:
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```text
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Correct spelling and grammar from the following text.\nI do not wan to go
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```
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But this one would:
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```text
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Correct spelling and grammar from the following text.\nI do not wan to go\n
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```
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## Hardware Requirements
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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.
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We did not notice any difference between the fp32 and fp16 versions in terms of quality. |