Update README.md

README.md

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 ---
 license: llama2
+library_name: transformers
+base_model: INX-TEXT/Bailong-instruct-7B
+pipeline_tag: text-generation
+tags:
+- orpo
 ---
 # Bailong-orpo 7B
 <div align="center">
@@ -18,7 +23,16 @@ This model card contains the information and the results of our released Bailong
 - **Bailong-bench:** Most existing language models claiming to support Traditional Chinese are adapted from continuously pre-trained open-source models, primarily trained on English data. In certain cases, models fine-tuned with instructions using this approach may respond to Traditional Chinese instructions in English and vice versa. This could pose a significant problem when deploying the model for real-world applications. Consequently, it is essential to have a benchmark dataset specifically designed to assess a model's proficiency in following both English and Traditional Chinese instructions. To address this issue, we propose Bailong-bench, a benchmark dataset crafted not only to evaluate the model's performance in various real-world application scenarios but also to assess its ability to maintain language consistency.
 - **Technical report:** In our [technical report](https://arxiv.org/abs/2404.00862), we document the model training process and the details regarding the sources of training data.
 - **Bailong-orpo 7B:** Leveraging monolithic odds ratio preference optimization
-algorithm, [ORPO](https://arxiv.org/abs/2403.07691), we further fine-tune Bailong-instruct 7B with 180k preference training data to derive Bailong-orpo 7B.
+algorithm, [ORPO](https://arxiv.org/abs/2403.07691), we further fine-tune Bailong-instruct 7B with 180k preference pair data to derive Bailong-orpo 7B. We also provide f16 GGUF version of Bailong-orpo 7B for efficient inference and storage purposes.
+
+## Model information
+- License: [Llama-2 License](https://ai.meta.com/llama/license/)
+- Base Model: [INX-TEXT/Bailong-instruct-7B](https://huggingface.co/INX-TEXT/Bailong-instruct-7B)
+- Type: decoder-only transformer architecture
+- Model Size: 6.96B
+- Context length: 2048
+- Vocabulary size: 59241
+- Language: English and Traditional Chinese
 
 ## Applications (Bailong-orpo 7B)
 The following tables present, but are not limited to, several possible scenarios for the applications of Bailong-orpo 7B. All the following model outputs are generated under the same generation configuration (temperature=0.6, top-p=0.9, top-k=40, repetition_penalty=1.1)