Create README.md

README.md

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+---
+base_model: yentinglin/Taiwan-LLM-13B-v2.0-chat
+inference: false
+language:
+  - zh
+library_name: transformers
+license: apache-2.0
+model_creator: yentinglin
+model_name: Taiwan-LLM-13B-v2.0
+model_type: llama
+pipeline_tag: text-generation
+quantized_by: yentinglin
+tags:
+- finetuned
+---
+# TaiwanLLM 13B v2.0 - AWQ
+- Model creator: [Yenting Lin](https://huggingface.co/yentinglin)
+- Original model: [TaiwanLLM 13B v2.0](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat)
+
+<!-- description start -->
+## Description
+
+This repo contains AWQ model files for [TaiwanLLM 13B v2.0](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat).
+
+
+### About AWQ
+
+AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
+
+AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
+
+It is supported by:
+
+- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
+- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
+- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
+- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
+- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
+
+<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 [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟
+
+# Model Card for Taiwan LLM 13B v2.0 chat
+
+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).
+
+
+## Model description
+
+- **Model type:** A 13B 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-13B-v2.0-base](https://huggingface.co/yentinglin/Taiwan-LLM-13B-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)
+
+TMMLUS+ score: 24.76727075757576
+
+## 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="yentinglin/Taiwan-LLM-13B-v2.0-chat", 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:
+
+```
+@misc{lin2023taiwan,
+      title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model}, 
+      author={Yen-Ting Lin and Yun-Nung Chen},
+      year={2023},
+      eprint={2311.17487},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+# Acknowledgement
+
+Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable compute resources for the project.