togethercomputer
/

Llama-2-7B-32K-Instruct

@@ -20,20 +20,20 @@ We hope that this can enable everyone to finetune their own version of [LLaMA-2-
 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
-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.
 Alternatively, you can load the model directly from the Hugging Face model hub using
 ```python
@@ -43,7 +43,7 @@ tokenizer = AutoTokenizer.from_pretrained("togethercomputer/LLaMA-2-7B-32K-Chat"
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/LLaMA-2-7B-32K-Chat", 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].

 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 (in this case, [Llama-2-70B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)).
+   The complete dataset is also released [here](https://huggingface.co/datasets/togethercomputer/llama-instruct).
+   We also share the complete recipe for the data collection process [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.
+   We used 4K of the instructions in our fine-tuning.
 ## Model Usage
+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.
 Alternatively, you can load the model directly from the Hugging Face model hub using
 ```python
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/LLaMA-2-7B-32K-Chat", 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].