Upload README.md

README.md

@@ -0,0 +1,120 @@
+---
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- zh
+- en
+---
+
+# Model Card for MediaTek Research Breeze-7B-32k-Base-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** | **DRCD-64k** |
+|---------------------------|----------|--------------|--------------|--------------|
+| **Breeze-7B-32k-Instruct-v1\_0** |     |      |       |        |
+| **Breeze-7B-32k-Base-v1\_0** | 79.73    | 69.68        | 61.55        | 25.82        |
+| **Breeze-7B-Base-v1\_0**      | 80.61    | 21.79        | 15.29        | 12.63        |
+
+#### Short-Benchmark Performance
+
+| **Model/Performance(EM)** | **TMMLU+** | **MMLU** | **TABLE** | 
+|---------------------------|----------|--------------|--------------|
+| **Breeze-7B-32k-Instruct-v1\_0** |     |      |       |        |
+| **Breeze-7B-Instruct-v1\_0**      |    |       |        |    |
+
+## Use in Transformers
+
+First install direct dependencies:
+```
+pip install transformers torch accelerate
+```
+If you want faster inference using flash-attention2, you need to install these dependencies:
+```bash
+pip install packaging ninja
+pip install flash-attn
+```
+Then load the model in transformers:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "MediaTek-Research/Breeze-7B-32k-Base-v1_0",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2" # optional but highly recommended
+)
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("MediaTek-Research/Breeze-7B-32k-Base-v1_0")
+tokenizer.tokenize("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。")
+# Tokenized results
+# ['▁', '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', '。']
+```
+
+
+## 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}
+}
+```