Update README.md

README.md

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----
-license: apache-2.0
----
+---
+library_name: transformers
+license: apache-2.0
+datasets:
+- liswei/Taiwan-Text-Excellence-2B
+- liswei/PromptPair-TW
+- yentinglin/TaiwanChat
+base_model: apple/OpenELM
+language:
+- zh
+metrics:
+- perplexity
+pipeline_tag: text-generation
+---
+
+<center>
+    <img src="https://huggingface.co/liswei/Taiwan-ELM/resolve/main/Taiwan%20ELM%20Logo.jpeg" alt="Efficient LLM for Taiwan">
+</center>
+
+> Efficient LLM for Taiwan
+
+# Taiwan ELM
+
+Taiwan ELM is a family of Efficient LLMs for Taiwan base on [apple/OpenELM](https://huggingface.co/apple/OpenELM).
+The project aims to provide an efficient model for researchers without access to large-scale computing resources.
+
+The model is trained using a custom fork of [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) on 2B Traditional Chinese tokens and 500K instruction samples.
+We will extend the model to train on larger data sets and different base models if there is sufficient demand.
+
+## What is being released?
+
+We release both pre-trained base models and instruction tuned variants with 270M and 1.1B parameters.
+Along with the model, datasets used to train the base and instruction-tuned models are also released.
+
+List of released models:
+* Taiwan-ELM-270M
+* Taiwan-ELM-1_1B
+* Taiwan-ELM-270M-Instruct
+* Taiwan-ELM-1_1B-Instruct
+
+List of released datasets:
+* [liswei/Taiwan-Text-Excellence-2B](https://huggingface.co/datasets/liswei/Taiwan-Text-Excellence-2B)
+* [liswei/PromptPair-TW](https://huggingface.co/datasets/liswei/PromptPair-TW)
+
+## Usage Examples
+
+We adapt the LLaMA2 template:
+```jinja2
+<s>[INST] <<SYS>>
+{{ system_prompt }}
+<</SYS>>
+
+{{ user_message }} [/INST]
+```
+
+The model could be load via `AutoModelForCausalLM` with `trust_remote_code=True`:
+```python
+taiwanelm_270m = AutoModelForCausalLM.from_pretrained("liswei/Taiwan-ELM-270M", trust_remote_code=True)
+```
+
+We also support additional generation methods and speculative generation, please find reference at [OpenELM#usage](https://huggingface.co/apple/OpenELM#usage).