Update README.md

README.md

@@ -7,17 +7,17 @@ datasets:
 - togethercomputer/llama-instruct
 ---
 
-# LLaMA-2-7B-32K-Chat
+# LLaMA-2-7B-32K-Instruct
 
 ## 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 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](https://github.com/togethercomputer/LLaMA-2-32K-Chat).
+LLaMA-2-7B-32K-Instruct 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-Instruct 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](https://github.com/togethercomputer/LLaMA-2-32K-Instruct).
 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! 
 
 ## Data Collection Details
 
-LLaMA-2-7B-32K-Chat is fine-tuned over a combination of two parts:
+LLaMA-2-7B-32K-Instruct 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).
@@ -36,8 +36,8 @@ Alternatively, you can load the model directly from the Hugging Face model hub u
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-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)
+tokenizer = AutoTokenizer.from_pretrained("togethercomputer/LLaMA-2-7B-32K-Instruct")
+model = AutoModelForCausalLM.from_pretrained("togethercomputer/LLaMA-2-7B-32K-Instruct", trust_remote_code=True, torch_dtype=torch.float16)
 input_ids = tokenizer.encode(<your instruction>, return_tensors="pt")
 output = model.generate(input_ids, max_length=..., temperature=...)
 output_text = tokenizer.decode(output[0], skip_special_tokens=True)
@@ -90,25 +90,25 @@ We evaluate the model from three aspects: 1) [Normalized perplexity](https://tog
 | Model | 2K Seq | 4K Seq | 8K Seq | 16K Seq | 32K Seq |
 | -------- | ------- | ------- | ------- | ------- | ------- |
 | LLaMA-2-7B-Chat (Meta) | 1.844 | 1.833 | N/A | N/A | N/A |
-| LLaMA-2-7B-32K-Chat (ours) | 1.813 | 1.798 | 1.781 | 1.778 | 1.772|
+| LLaMA-2-7B-32K-Instruct (ours) | 1.813 | 1.798 | 1.781 | 1.778 | 1.772|
 
 * Rouge Score over BookSum
 | Model | R1 | R2 | RL |
 | -------- | ------- | ------- | ------- |
 | LLaMA-2-7B-Chat (Meta) | 0.055 | 0.008 | 0.046 |
-| LLaMA-2-7B-32K-Chat (ours) | 0.365 | 0.086 | 0.192 |
+| LLaMA-2-7B-32K-Instruct (ours) | 0.365 | 0.086 | 0.192 |
 
 * Accuracy over MQA
 | Model | 20 docs (Avg 2.9K tokens) | 30 docs (Avg 4.4K tokens) | 50 docs (Avg 7.4K tokens) |
 | -------- | ------- | ------- | ------- |
 | LLaMA-2-7B-Chat (Meta) | 0.384 | 0.375 | 0.313 |
-| LLaMA-2-7B-32K-Chat (ours) | 0.451 | 0.434 | 0.373 |
+| LLaMA-2-7B-32K-Instruct (ours) | 0.451 | 0.434 | 0.373 |
 
-We observe that LLaMA-2-7B-32K-Chat obtains reasonable (and even better) perplexity, rouge score and accuracy over the original LLaMA-2-7B-Chat model.
+We observe that LLaMA-2-7B-32K-Instruct obtains reasonable (and even better) perplexity, rouge score and accuracy over the original LLaMA-2-7B-Chat model.
 
 ## Limitations and Bias
 
-As with all language models, LLaMA-2-7B-32K-Chat may generate incorrect or biased content. It's important to keep this in mind when using the model.
+As with all language models, LLaMA-2-7B-32K-Instruct may generate incorrect or biased content. It's important to keep this in mind when using the model.
 
 ## Community