Update README.md

README.md

@@ -44,9 +44,11 @@ model-index:
 
 
 # replit-code-v1-3b
+Developed by: Replit, Inc.
 
 [**🧑‍💻 Test it on our Demo Space! 🧑‍💻**](https://huggingface.co/spaces/replit/replit-code-v1-3b-demo)
 
+## Model Description
 `replit-code-v1-3b` is a 2.7B Causal Language Model focused on **Code Completion**. The model has been trained on a subset of the [Stack Dedup v1.2 dataset](https://arxiv.org/abs/2211.15533).
 
 The training mixture includes **20 different languages**, listed here in descending order of number of tokens: 
@@ -55,10 +57,19 @@ The training mixture includes **20 different languages**, listed here in descend
 
 In total, the training dataset contains 175B tokens, which were repeated over 3 epochs -- in total, `replit-code-v1-3b` has been trained on **525B** tokens (~195 tokens per parameter).
 
+## Intended Use
+Replit intends this model be used by anyone as a foundational model for application-specific fine-tuning without strict limitations on commercial use.
 
-## How to use the model
+## Limitations
+The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters, and such content may be reflected in model generated text. We recommend that users exercise reasonable caution when using in production systems. Do not use for any applications that may cause harm or distress to individuals or groups.
 
+## License
+The base model checkpoint is licensed under the Creative Commons license (CC BY-SA-4.0).  Under the license, you must give credit to Replit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests that Replit endorses you or your use.
 
+## Contact
+For questions and comments about the model, please post in the community section. 
+
+## How to Use
 ```python
 from transformers import AutoModelForCausalLM
 
@@ -85,7 +96,7 @@ y = model(x)
 Note that `trust_remote_code=True` is passed to the `from_pretrained` method because ReplitLM is not a class in the
 [Transformers](https://huggingface.co/docs/transformers/index) library. 
 
-## Tokenizer
+### Tokenizer
 
 We have trained a custom SentencePiece Unigram tokenizer optimized with a vocabulary specifically for code of 32768 tokens.
 
@@ -113,7 +124,7 @@ Note that:
 - `clean_up_tokenization_spaces=False` is meant to avoid removing spaces in the output, because that would affect the syntactical correctness of the generated code. 
 
 
-## Generation
+### Generation
 
 You can generate code using the `transformers` library as follows:
 
@@ -131,7 +142,7 @@ print(generated_code)
 
 Experiment with different decoding methods and parameters to get the best results for your use case.
 
-## Post Processing
+### Post Processing
 
 Note that as with all code generation models, post-processing of the generated code is important. In particular, the following post-processing steps are recommended:
 - stop generation when the EOS token is encountered
@@ -139,5 +150,7 @@ Note that as with all code generation models, post-processing of the generated c
 - set `max_tokens` to a reasonable value based on your completion use case
 - truncate generation to stop words such as `return`, `def`, "```", "`\n\n\n`" to avoid generating incomplete code when `max_tokens` is larger than the length of the expected generated code.
 
+
+
 ## Model Hash
 5bc28ce32c6f9aec935ead7b60ea1c46