Update README.md

README.md

@@ -36,7 +36,7 @@ Results in ConvRAG Bench are as follows:
 | Average (all) | 47.71 | 50.93 | 52.52 | 53.90 | 54.14 | 55.17 | 58.25 |
 | Average (exclude HybriDial) | 46.96 | 51.40 | 52.95 | 54.35 | 53.89 | 53.99 | 57.14 |
 
-Note that ChatQA-1.5 used some samples from the HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial. The data and evaluation scripts for ConvRAG can be found [here](https://huggingface.co/datasets/nvidia/ConvRAG-Bench).
+Note that ChatQA-1.5 is built based on Llama-3 base model, and ChatQA-1.0 is built based on Llama-2 base model. We used some samples from the HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial. The data and evaluation scripts for ConvRAG can be found [here](https://huggingface.co/datasets/nvidia/ConvRAG-Bench).
 
 
 ## Prompt Format
@@ -57,13 +57,13 @@ Assistant:
 
 ## How to use
 
-### take the whole document as context
+### take the whole document as context 
 This can be applied to the scenario where the whole document can be fitted into the model, so that there is no need to run retrieval over the document.
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-model_id = "nvidia/ChatQA-1.5-70B"
+model_id = "nvidia/Llama3-ChatQA-1.5-70B"
 
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
@@ -104,7 +104,7 @@ print(tokenizer.decode(response, skip_special_tokens=True))
 ```
 
 ### run retrieval to get top-n chunks as context
-This can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our [Dragon-multiturn](https://huggingface.co/nvidia/dragon-multiturn-query-encoder) retriever which can handle conversatinoal query. In addition, we provide a few [documents](https://huggingface.co/nvidia/ChatQA-1.5-70B/tree/main/docs) for users to play with.
+This can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our [Dragon-multiturn](https://huggingface.co/nvidia/dragon-multiturn-query-encoder) retriever which can handle conversatinoal query. In addition, we provide a few [documents](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B/tree/main/docs) for users to play with.
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
@@ -112,7 +112,7 @@ import torch
 import json
 
 ## load ChatQA-1.5 tokenizer and model
-model_id = "nvidia/ChatQA-1.5-70B"
+model_id = "nvidia/Llama3-ChatQA-1.5-70B"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")