Update README.md

README.md

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 # GPT-NeoXT-Chat-Base-20B
 
-GPT-NeoXT-Chat-Base-20B is a 20B parameter open source chat model, fine-tuned from EleutherAI’s GPT-NeoX with over 40 million instructions on 100% carbon negative compute. 
-It is part of OpenChatKit (codebase available [here](https://github.com/togethercomputer/OpenChaT))
-a community project that enables the open source AI contributors to improve the datasets available for training large language models and chatbots.
+> TLDR: As part of OpenChatKit (codebase available [here](https://github.com/togethercomputer/OpenChaT)),
+> GPT-NeoXT-Chat-Base-20B is a 20B parameter language model, fine-tuned from EleutherAI’s GPT-NeoX with over 40 million instructions on 100% carbon negative compute.
+
+We base GPT-NeoXT-Chat-Base-20B on ElutherAI’s GPT-NeoX model, and fine-tune it with data focusing on dialog-style interactions. 
+We focused the tuning on several tasks such as question answering, classification, extraction, and summarization. 
+We’ve fine-tuned the model with a collection of 43 million high-quality instructions. 
+Together partnered with LAION and Ontocord, who both helped curate the dataset the model is based on. 
+You can read more about this process and the availability of this dataset in LAION’s blog post [here](...). 
 
 ## Model Details
 - **Developed by**: \[TODO\] Together Computer, LAION, Ontocord, ...
@@ -21,6 +26,13 @@ a community project that enables the open source AI contributors to improve the
 ## Examples
 \[TODO\] sync with the blog post
 
+## Training Examples
+
+The training data consists of pairs of human queries and corresponding bot responses, with human queries prefixed with <human>: and bot responses prefixed with <bot>:.
+An example of the data format is as follows:
+
+
+
 # Uses
 \[TODO\]
 
@@ -78,11 +90,9 @@ We therefore welcome contributions from individuals and organizations, and encou
 \[TODO\]
 
 **Training Procedure**
-\[TODO\]
 
-\[TODO\]
 - **Hardware:** 2 x 8 x A100 GPUs
-- **Optimizer:** AdamW
+- **Optimizer:** [8bit-AdamW](https://github.com/TimDettmers/bitsandbytes)
 - **Gradient Accumulations**: 2
 - **Batch:** 2 x 2 x 64 x 2048 = 524288 tokens
 - **Learning rate:** warmup to 1e-6 for 100 steps and then kept constant