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Model: nlpcloud/instruct-gpt-j-fp16
Source: Original Platform

README.md

@@ -0,0 +1,103 @@
+---
+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.