jcai / breeze-7b-32k-instruct-v1_0

Breeze-7B-32k-Instruct是从基础模型Breeze-7B-32k-Base推导而来，使得生成的模型可以用于常见的任务。〈f16, Q4, Q4_K_M〉

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README

MediaTek Research Breeze-7B-32k-Instruct-v1_0 模型卡片

来源：链接

MediaTek Research Breeze-7B（以下简称为 Breeze-7B）是建立在Mistral-7B之上，专门为繁体中文使用而定制的一个语言模型系列。

Breeze-7B-Base是 Breeze-7B 系列的基础模型。
如果你有大量的微调数据，将其用于特定用例的微调是适合的。

Breeze-7B-Instruct源自基础模型 Breeze-7B-Base，使得所产生的模型可以直接用于常见任务。

Breeze-7B-32k-Base是在基础模型的基础上扩展的，拥有更多的数据、基础改动和禁用滑动窗口。
大致相当于44k个繁体中文字符。

Breeze-7B-32k-Instruct源自基础模型 Breeze-7B-32k-Base，使得所产生的模型可以直接用于常见任务。

实用性方面
- Breeze-7B-Base版本通过增加3万个繁体字对原有的词汇进行了扩展。在词汇量扩展的情况下，Breeze-7B对于处理繁体中文信息，推理速度是Mistral-7B和Llama 7B的两倍。[见推理性能。]
- Breeze-7B-Instruct可以直接用于常见的任务如问答、阅读理解、多轮对话和摘要。
- Breeze-7B-32k-Instruct能够在文档级别进行任务（对于中文，为20至40页）。

本项目由以下成员完成（按字母顺序）：許湛然、廖峰挺、許博竣、陳宜昌，指导老师許大山。

功能特点

Breeze-7B-32k-Base-v1_0
- 将词汇字典大小从32k扩展到62k，以更好地支持繁体中文
- 32k词元上下文长度
Breeze-7B-32k-Instruct-v1_0
- 将词汇字典大小从32k扩展到62k，以更好地支持繁体中文
- 32k词元上下文长度
- 多轮对话（无需处理危害性）

模型详细信息

Breeze-7B-32k-Base-v1_0
- 预训练自：Breeze-7B-Base
- 模型类型：只有因果解码器的Transformer语言模型
- 语言：英语和繁体中文（zh-tw）
Breeze-7B-32k-Instruct-v1_0
- 微调自：Breeze-7B-32k-Base
- 模型类型：只有因果解码器的Transformer语言模型
- 语言：英语和繁体中文（zh-tw）

长上下文性能

在草堆中找针的性能

我们使用密钥检索任务来测试模型在给定序列中关注不同深度的能力。
在长上下文中放置了一个密钥让模型检索。
密钥位置被分成了16个区间，每个区间有20个测试案例。
Breeze-7B-32k-Base以90%以上的准确率完成了任务，见下图中。

长DRCD性能

模型/性能（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

短期基准性能

模型/性能（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

引用

@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}
}

## Model Card for MediaTek Research Breeze-7B-32k-Instruct-v1_0

## Source: [Link](https://hugging-face.cn/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://hugging-face.cn/mistralai/Mistral-7B-v0.1), specifically intended for Traditional Chinese use.

[Breeze-7B-Base](https://hugging-face.cn/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://hugging-face.cn/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://hugging-face.cn/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://hugging-face.cn/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://hugging-face.cn/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://hugging-face.cn/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://hugging-face.cn/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 |

## 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}
}
```

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