Update README.md

README.md

@@ -23,17 +23,106 @@ extra_gated_fields:
   Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author: checkbox
   使用Taiwan LLM必須明確地承認和歸功於優必達株式會社 Ubitus 以及原始作者: checkbox
 ---
+<img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png" alt="Taiwan LLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
-# 🌟 Checkout New [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟
-# Taiwan LLM based on Mistral-7B-v0.1
+# 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟
 
-continue pretraining on 20 billion tokens in traditional Mandarin and instruction fine-tuning on millions of conversations.
+# Model Card for Taiwan LLM 7B v2.1 chat
 
-This version does NOT include CommonCrawl.
+Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan. 
+Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning. 
+This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances. 
+It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance. 
+For detailed insights into Taiwan LLM's development and features, refer to our [technical report](https://github.com/MiuLab/Taiwan-LLaMa/blob/main/twllm_paper.pdf).
 
-# Collaboration with Ubitus K.K. 💪💪💪
 
-本項目與 Ubitus K.K. 合作進行。Ubitus 為本項目提供寶貴的技術支持和計算資源。
+## Model description
 
-Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable technical support and compute resources for the project.
+- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
+- **Language(s) (NLP):** Primarily Traditional Chinese (zh-tw)
+- **Finetuned from model:** [yentinglin/Taiwan-LLM-7B-v2.0-base](https://huggingface.co/yentinglin/yentinglin/Taiwan-LLM-7B-v2.0-base)
 
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/MiuLab/Taiwan-LLaMa
+- **Demo:** https://twllm.com/
+
+## Performance
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/HTwIzw6RDha2-PhuWqSuI.png)
+
+## Intended uses
+
+Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
+
+```python
+# pip install transformers>=4.34
+# pip install accelerate
+
+import torch
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")
+
+# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
+messages = [
+    {
+        "role": "system",
+        "content": "你是一個人工智慧助理",
+    },
+    {"role": "user", "content": "東北季風如何影響台灣氣候？"},
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
+
+### Training hyperparameters
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/MdvHwdUvH-c926qyRAw7K.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/kKpkvxDzOEyiAoTqmzRYO.png)
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/FsnlJ_fkRxf7fn5RKZnjE.png)
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- distributed_type: multi-GPU
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 5.0
+
+## Citation
+
+If you find Taiwan LLM is useful in your work, please cite it with:
+
+```
+@inproceedings{lin-chen-2023-llm,
+    title = "{LLM}-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models",
+    author = "Lin, Yen-Ting  and Chen, Yun-Nung",
+    booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.nlp4convai-1.5",
+    pages = "47--58"
+}
+
+@misc{taiwanllama,
+    author={Lin, Yen-Ting and Chen, Yun-Nung},
+    title={Language Models for Taiwanese Culture},
+    year={2023},
+    url={https://github.com/MiuLab/Taiwan-LLaMa},
+    note={Code and models available at https://github.com/MiuLab/Taiwan-LLaMa},
+}
+```
+
+# Acknowledgement
+
+Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable compute resources for the project.