Update README.md

README.md

@@ -4,7 +4,7 @@ license_link: https://huggingface.co/LL-Square/LLSquare-7B-Instruct
 language:
 - en
 pipeline_tag: text-generation
-base_model: Qwen/Qwen2.5-7B
+base_model: LL-Square/LLSquare-7B-Instruct-7B
 tags:
 - chat
 library_name: transformers
@@ -17,14 +17,14 @@ library_name: transformers
 
 ## Introduction
 
-Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
+LLSquare-7B-Instruct is the base model of LL-Square large language models. For LLSquare-7B-Instruct, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. LLSquare-7B-Instruct brings the following improvements upon LL-Square2:
 
 - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
 - Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
 - **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
 - **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. 
 
-**This repo contains the instruction-tuned 7B Qwen2.5 model**, which has the following features:
+**This repo contains the instruction-tuned 7B LLSquare-7B-Instruct model**, which has the following features:
 - Type: Causal Language Models
 - Training Stage: Pretraining & Post-training
 - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
@@ -33,17 +33,16 @@ Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we rele
 - Number of Layers: 28
 - Number of Attention Heads (GQA): 28 for Q and 4 for KV
 - Context Length: Full 131,072 tokens and generation 8192 tokens
-  - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
+  - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy LLSquare-7B-Instruct for handling long texts.
 
-For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
 
 ## Requirements
 
-The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
+The code of LLSquare-7B-Instruct has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
 
 With `transformers<4.37.0`, you will encounter the following error:
 ```
-KeyError: 'qwen2'
+KeyError: 'LL-Square2'
 ```
 
 ## Quickstart
@@ -53,7 +52,7 @@ Here provides a code snippet with `apply_chat_template` to show you how to load
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "Qwen/Qwen2.5-7B-Instruct"
+model_name = "LL-Square/LLSquare-7B-Instruct-7B-Instruct"
 
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -64,7 +63,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 prompt = "Give me a short introduction to large language model."
 messages = [
-    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "system", "content": "You are LL-Square, created by Alibaba Cloud. You are a helpful assistant."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
@@ -103,31 +102,31 @@ For supported frameworks, you could add the following to `config.json` to enable
 ```
 
 For deployment, we recommend using vLLM. 
-Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
+Please refer to our [Documentation](https://LL-Square.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
 Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. 
 We advise adding the `rope_scaling` configuration only when processing long contexts is required.
 
 ## Evaluation & Performance
 
-Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
+Detailed evaluation results are reported in this [📑 blog](https://LL-Squarelm.github.io/blog/LLSquare-7B-Instruct/).
 
-For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+For requirements on GPU memory and the respective throughput, see results [here](https://LL-Square.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
 
 ## Citation
 
 If you find our work helpful, feel free to give us a cite.
 
 ```
-@misc{qwen2.5,
-    title = {Qwen2.5: A Party of Foundation Models},
-    url = {https://qwenlm.github.io/blog/qwen2.5/},
-    author = {Qwen Team},
+@misc{LLSquare-7B-Instruct,
+    title = {LLSquare-7B-Instruct: A Party of Foundation Models},
+    url = {https://LL-Squarelm.github.io/blog/LLSquare-7B-Instruct/},
+    author = {LL-Square Team},
     month = {September},
     year = {2024}
 }
 
-@article{qwen2,
-      title={Qwen2 Technical Report}, 
+@article{LL-Square2,
+      title={LL-Square2 Technical Report}, 
       author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
       journal={arXiv preprint arXiv:2407.10671},
       year={2024}