Name	Quant method	Bits	Size	Use case
Breeze-7B-32k-Instruct-v1_0-Q4_K_M.gguf	Q4_K_M	4	4.54 GB	medium, balanced quality - recommended
Breeze-7B-32k-Instruct-v1_0-Q5_0.gguf	Q5_0	5	5.18 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Breeze-7B-32k-Instruct-v1_0-Q5_K_M.gguf	Q5_K_M	5	5.32 GB	large, very low quality loss - recommended
Breeze-7B-32k-Instruct-v1_0-Q5_K_S.gguf	Q5_K_S	5	5.18 GB	large, low quality loss - recommended
Breeze-7B-32k-Instruct-v1_0-Q6_K.gguf	Q6_K	6	6.14 GB	very large, extremely low quality loss

Original model card

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, specifically intended for Traditional Chinese use.

Breeze-7B-Base 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 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 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 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.]
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
- 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
- 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.

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

Flash-attention2 is strongly recommended for long context scenarios.

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}
}

chienweichang/Breeze-7B-32k-Instruct-v1_0-GGUF

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