Update README: add the details for data collections and fix typos

README.md

@@ -11,24 +11,40 @@ datasets:
 
 ## Model Description
 
-LLaMA-2-7B-32K-Chat is an open-source, long-context chat model finetuned from [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K), over high-quality instructions and chat data.
-We build Llama-2-7B-32K-Chat with less than 200 lines of Python script using [Together API](https://together.ai/blog/api-announcement), and we also make the recipe fully available. 
+LLaMA-2-7B-32K-Chat is an open-source, long-context chat model finetuned from [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K), over high-quality instruction and chat data.
+We built Llama-2-7B-32K-Chat with less than 200 lines of Python script using [Together API](https://together.ai/blog/api-announcement), and we also make the recipe fully available.
+For more details, please refer to our [Github repo](https://github.com/togethercomputer/LLaMA-2-32K-Chat).
 We hope that this can enable everyone to finetune their own version of [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) — play with [Together API](https://together.ai/blog/api-announcement) and give us feedback! 
+For more details 
 
+## Data Collection Details
 
-## Data Collection
-
-LLaMA-2-7B-32K-Chat is fine-tuned over two datasets: (1) 19K single- and multi-round conversations generated by human instructions and 
-Llama-2-70B-Chat outputs, along with (2) 4K instructions of summarization from the BookSum datasets. 
-We collected dataset (1) following the distillation paradigm that is used by Alpaca, Vicuna, WizardLM, Orca — producing instructions by querying a powerful LLM.
-We release such dataset [here](https://huggingface.co/datasets/togethercomputer/llama-instruct). For more details, please refer to our [Github repo](https://github.com/togethercomputer/LLaMA-2-32K-Chat).
+LLaMA-2-7B-32K-Chat is fine-tuned over a combination of two parts:
+1. **19K single- and multi-round conversations generated by human instructions and [Llama-2-70B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) outputs**.
+   We collected the dataset following the distillation paradigm that is used by Alpaca, Vicuna, WizardLM, Orca — producing instructions by querying a powerful LLM.
+   This dataset is also released [here](https://huggingface.co/datasets/togethercomputer/llama-instruct).
+   We also share the complete collection recipe [here](https://github.com/togethercomputer/LLaMA-2-32K-Chat).
+   
+3. **4K instructions of summarization from the BookSum datasets**.
+   BookSum is a unique dataset designed to address the challenges of long-form narrative summarization.
+   This dataset features source documents from the literature domain, including novels, plays, and stories, and offers human-written, highly abstractive summaries.
+   We here focus on chapter-level data. BookSum poses a unique set of challenges, necessitating that the model comprehensively read through each chapter.
 
 
 ## Model Usage
 
-You can use the [Together API](https://together.ai/blog/api-announcement) to try out LLaMA-2-7B-32K-Chat for inference. 
+We encourage you to try out this model using the [Together API](https://together.ai/blog/api-announcement). 
 The updated inference stack allows for efficient inference.
-The model will also be hosted on [Together Playground](https://api.together.xyz/playground). You can simply play with the model by using prompt formatted by 
+Alternatively, you can load the model directly from the Hugging Face model hub using
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("togethercomputer/LLaMA-2-7B-32K")
+model = AutoModelForCausalLM.from_pretrained("togethercomputer/LLaMA-2-7B-32K", trust_remote_code=True, torch_dtype=torch.float16)
+```
+
+The model is also hosted on [Together Playground](https://api.together.xyz/playground). You can simply play with the model by using prompt formatted by 
 
 ```
 [INST] <your instruction here> [\INST].