updated the use section

README.md

@@ -38,17 +38,36 @@ dimension is split into 16 heads, each with a dimension of 256. Rotary Position
 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
 GPT-2/GPT-3.
 
-## Training data
+## Intended Use and Limitations
 
-GPT-J 6B was trained on [the Pile](https://pile.eleuther.ai), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai).
+GPT-J learns an inner representation of the English language that can be used to 
+extract features useful for downstream tasks. The model is best at what it was 
+pretrained for however, which is generating text from a prompt.
 
-## Training procedure
+### Out-of-scope use
 
-This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly.
+GPT-J-6B is **not** intended for deployment without fine-tuning, supervision, 
+and/or moderation. It is not a in itself a product and cannot be used for 
+human-facing interactions. For example, the model may generate harmful or 
+offensive text. Please evaluate the risks associated with your particular use case.
 
-## Intended Use and Limitations
+GPT-J-6B was trained on an English-language only dataset, and is thus **not**
+suitable for translation or generating text in other languages.
+
+GPT-J-6B has not been fine-tuned for downstream contexts in which 
+language models are commonly deployed, such as writing genre prose, 
+or commercial chatbots. This means GPT-J-6B will **not** 
+respond to a given prompt the way a product like ChatGPT does. This is because,
+ unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement 
+Learning from Human Feedback (RLHF) to better “follow” human instructions.
+
+### Limitations and Biases
+
+The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output.
 
-GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt.
+GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile.
+
+As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
 
 ### How to use
 
@@ -61,13 +80,13 @@ tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
 model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
 ```
 
-### Limitations and Biases
+## Training data
 
-The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output.
+GPT-J 6B was trained on [the Pile](https://pile.eleuther.ai), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai).
 
-GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile.
+## Training procedure
 
-As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
+This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly.
 
 ## Evaluation results