Update README.md

README.md

@@ -20,7 +20,7 @@ The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion to
 We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
 
 #### This Model
-This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T). 
+This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-955k-2T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T). 
 The dataset used is [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25) following the [chatml](https://github.com/openai/openai-python/blob/main/chatml.md) format.
 #### How to use
 You will need the transformers>=4.31
@@ -29,7 +29,7 @@ Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for
 from transformers import AutoTokenizer
 import transformers 
 import torch
-model = "PY007/TinyLlama-1.1B-Chat-v0.4"
+model = "PY007/TinyLlama-1.1B-Chat-v0.5"
 tokenizer = AutoTokenizer.from_pretrained(model)
 pipeline = transformers.pipeline(
     "text-generation",