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

Breeze-7B-32k-Instruct是由基础模型Breeze-7B-32k-Base衍生而来，使得到的模型适用于常见任务。〈f16, Q4, Q4_K_M〉

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README

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

来源：链接

MediaTek 研究院 Breeze-7B（以下简称 Breeze-7B）是基于 Mistral-7B 进行的扩展，主要用于繁体中文的使用。

Breeze-7B-Base 是 Breeze-7B 系列的基础模型。
如果您的数据量足够大，适合进行大量的微调，则适合使用。

Breeze-7B-Instruct 从基础模型 Breeze-7B-Base 衍生而来，使得生成的模型适合直接用于常见任务。

Breeze-7B-32k-Base 从基础模型扩展，加入了更多数据、模型变更，并关闭了窗口滑动。
大致上，相当于44k繁体汉字。

Breeze-7B-32k-Instruct 从基础模型 Breeze-7B-32k-Base 衍生而来，使得生成的模型适合直接用于常见任务。

从实用性的角度来看


76.9	54.82	44.26
79.73	69.68	61.55
80.61	21.79	15.29


	41.37	61.34	34	5.8	7.4
	42.67	62.73	39.58	6.0	7.4

@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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