Update README.md

README.md

@@ -2,7 +2,7 @@
 license: other
 license_name: tongyi-qianwen-research
 license_link: >-
-  https://huggingface.co/Qwen/Qwen2-beta-4B-Chat/blob/main/LICENSE
+  https://huggingface.co/Qwen/Qwen1.5-4B-Chat/blob/main/LICENSE
 language:
 - en
 pipeline_tag: text-generation
@@ -10,12 +10,12 @@ tags:
 - chat
 ---
 
-# Qwen2-beta-4B-Chat
+# Qwen1.5-4B-Chat
 
 
 ## Introduction
 
-Qwen2-beta is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: 
+Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: 
 
 * 6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;
 * Significant performance improvement in human preference for chat models;
@@ -27,13 +27,13 @@ For more details, please refer to our blog post and GitHub repo.
 <br>
 
 ## Model Details
-Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.
+Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.
 
 ## Training details
 We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. However, DPO leads to improvements in human preference evaluation but degradation in benchmark evaluation. In the very near future, we will fix both problems.
 
 ## Requirements
-The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
+The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
 ```
 KeyError: 'qwen2'
 ```
@@ -47,10 +47,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda" # the device to load the model onto
 
 model = AutoModelForCausalLM.from_pretrained(
-    "Qwen/Qwen2-beta-4B-Chat",
+    "Qwen/Qwen1.5-4B-Chat",
     device_map="auto"
 )
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-beta-4B-Chat")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-4B-Chat")
 
 prompt = "Give me a short introduction to large language model."
 messages = [
@@ -75,7 +75,7 @@ generated_ids = [
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 
-For quantized models, we advise you to use the GPTQ, AWQ, and GGUF correspondents, namely `Qwen-beta-4B-Chat-GPTQ`, `Qwen-beta-4B-Chat-AWQ`, and `Qwen-beta-4B-Chat-GGUF`.
+For quantized models, we advise you to use the GPTQ, AWQ, and GGUF correspondents, namely `Qwen1.5-4B-Chat-GPTQ`, `Qwen1.5-4B-Chat-AWQ`, and `Qwen1.5-4B-Chat-GGUF`.
 
 
 ## Limitations