Update README.md

README.md

@@ -1,3 +1,132 @@
+
+From：
+
+https://huggingface.co/MediaTek-Research/Breeze-7B-32k-Instruct-v1_0
+
 ---
+pipeline_tag: text-generation
 license: apache-2.0
+language:
+- zh
+- en
 ---
+
+# Model Card for MediaTek Research Breeze-7B-32k-Instruct-v1_0
+
+MediaTek Research Breeze-7B (hereinafter referred to as Breeze-7B) is a language model family that builds on top of [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1), specifically intended for Traditional Chinese use.
+
+[Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v1_0) is the base model for the Breeze-7B series. 
+It is suitable for use if you have substantial fine-tuning data to tune it for your specific use case.
+
+[Breeze-7B-Instruct](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0) derives from the base model Breeze-7B-Base, making the resulting model amenable to be used as-is for commonly seen tasks.
+
+[Breeze-7B-32k-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-32k-Base-v1_0) is extended from the base model with more data, base change, and the disabling of the sliding window. 
+Roughly speaking, that is equivalent to 44k Traditional Chinese characters.
+
+[Breeze-7B-32k-Instruct](https://huggingface.co/MediaTek-Research/Breeze-7B-32k-Instruct-v1_0) derives from the base model Breeze-7B-32k-Base, making the resulting model amenable to be used as-is for commonly seen tasks.
+
+
+
+Practicality-wise:
+- Breeze-7B-Base expands the original vocabulary with additional 30,000 Traditional Chinese tokens. With the expanded vocabulary, everything else being equal, Breeze-7B operates at twice the inference speed for Traditional Chinese to Mistral-7B and Llama 7B. [See [Inference Performance](#inference-performance).]
+- Breeze-7B-Instruct can be used as is for common tasks such as Q&A, RAG, multi-round chat, and summarization.
+- Breeze-7B-32k-Instruct can perform tasks at a document level (For Chinese, 20 ~ 40 pages).
+
+*A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Feng-Ting Liao 廖峰挺, Po-Chun Hsu 許博竣, Yi-Chang Chen 陳宜昌, and the supervisor Da-Shan Shiu 許大山.*
+
+## Features
+
+- Breeze-7B-32k-Base-v1_0
+  - Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
+  - 32k-token context length
+ 
+- Breeze-7B-32k-Instruct-v1_0
+  - Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
+  - 32k-token context length
+  - Multi-turn dialogue (without special handling for harmfulness)
+
+## Model Details
+
+- Breeze-7B-32k-Base-v1_0
+  - Pretrained from: [Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v1_0)
+  - Model type: Causal decoder-only transformer language model
+  - Language: English and Traditional Chinese (zh-tw)
+- Breeze-7B-32k-Instruct-v1_0
+  - Finetuned from: [Breeze-7B-32k-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-32k-Base-v1_0)
+  - Model type: Causal decoder-only transformer language model
+  - Language: English and Traditional Chinese (zh-tw)
+
+## Long-context Performance
+
+#### Needle-in-a-haystack Performance
+
+We use the passkey retrieval task to test the model's ability to attend to different various depths in a given sequence.
+A key in placed within a long context distracting document for the model to retrieve.
+The key position is binned into 16 bins, and there are 20 testcases for each bin.
+Breeze-7B-32k-Base clears the tasks with 90+% accuracy, shown in the figure below.
+![Needle-in-a-haystack Performance](https://huggingface.co/MediaTek-Research/Breeze-7B-32k-Base-v1_0/resolve/main/needle-in-a-haystack-performance.png)
+
+#### Long-DRCD Performance
+
+| **Model/Performance(EM)** | **DRCD** | **DRCD-16k** | **DRCD-32k** |
+|---------------------------|----------|--------------|--------------|
+| **Breeze-7B-32k-Instruct-v1\_0** |  76.9   |   54.82   |  44.26     |       
+| **Breeze-7B-32k-Base-v1\_0** | 79.73    | 69.68        | 61.55        |
+| **Breeze-7B-Base-v1\_0**      | 80.61    | 21.79        | 15.29        | 
+
+#### Short-Benchmark Performance
+
+| **Model/Performance(EM)** | **TMMLU+** | **MMLU** | **TABLE** | **MT-Bench-tw** | **MT-Bench** |
+|---------------------------|----------|--------------|--------------|-----|-----|
+| **Breeze-7B-32k-Instruct-v1\_0** | 41.37    |   61.34   |   34    | 5.8 | 7.4 |
+| **Breeze-7B-Instruct-v1\_0**      |  42.67  |   62.73    |   39.58 | 6.0 | 7.4 |
+
+## Use in Transformers
+
+First, install direct dependencies:
+```
+pip install transformers torch accelerate
+```
+<p style="color:red;">Flash-attention2 is strongly recommended for long context scenarios.</p>
+
+```bash
+pip install packaging ninja
+pip install flash-attn
+```
+Then load the model in transformers:
+```python
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer
+>>> tokenizer = AutoTokenizer.from_pretrained("MediaTek-Research/Breeze-7B-32k-Instruct-v1_0/")
+>>> model = AutoModelForCausalLM.from_pretrained(
+>>>    "MediaTek-Research/Breeze-7B-32k-Instruct-v1_0",
+...    device_map="auto",
+...    torch_dtype=torch.bfloat16,
+...    attn_implementation="flash_attention_2"
+... )
+>>> chat = [
+...   {"role": "user", "content": "你好，請問你可以完成什麼任務？"},
+...   {"role": "assistant", "content": "你好，我可以幫助您解決各種問題、提供資訊和協助您完成許多不同的任務。例如：回答技術問題、提供建議、翻譯文字、尋找資料或協助您安排行程等。請告訴我如何能幫助您。"},
+...   {"role": "user", "content": "太棒了！"},
+... ]
+>>> tokenizer.apply_chat_template(chat, tokenize=False)
+"<s>You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan.   [INST] 你好，請問你可以完成什麼任務？ [/INST] 你好，我可以幫助您解決各種問題、提供資訊和協助您完成許多不同的任務。例如：回答技術問題、提供建議、翻譯文字、尋找資料或協助您安排行程等。請告訴我如何能幫助您。 [INST] 太棒了！ [/INST] "
+# Tokenized results
+# ['▁', '你好', '，', '請問', '你', '可以', '完成', '什麼', '任務', '？']
+# ['▁', '你好', '，', '我', '可以', '幫助', '您', '解決', '各種', '問題', '、', '提供', '資訊', '和', '協助', '您', '完成', '許多', '不同', '的', '任務', '。', '例如', '：', '回答', '技術', '問題', '、', '提供', '建議', '、', '翻譯', '文字', '、', '尋找', '資料', '或', '協助', '您', '安排', '行程', '等', '。', '請', '告訴', '我', '如何', '能', '幫助', '您', '。']
+# ['▁', '太', '棒', '了', '！']
+```
+
+
+
+## Citation
+
+```
+@article{MediaTek-Research2024breeze7b,
+      title={Breeze-7B Technical Report}, 
+      author={Chan-Jan Hsu and Chang-Le Liu and Feng-Ting Liao and Po-Chun Hsu and Yi-Chang Chen and Da-Shan Shiu},
+      year={2024},
+      eprint={2403.02712},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```