--- pipeline_tag: text-generation --- # Model Card for Breeze-7B-Instruct-v0.1 Breeze-7B-Instruct-v0.1 is a 7-billion-parameter language model built from Mistral-7B and tailored for Traditional Chinese (TC). This model incorporates additional 30k TC tokens in vocabulary dictionary to better adapt to TC and improve inference speed, resulting in a doubling of the original tokenizer's inference speed. Breeze-7B-Instruct-v0.1 performs well on both EN and TC benchmarks. This model outperforms Taiwan-LLM-7B-v2.1-chat, Taiwan-LLM-13B-v2.0-chat, and Yi-6B-Chat on major TC benchmarks we tested, and is comparable with Mistral-7B-Instruct-v0.1 on MMLU and MT-Bench in English. *A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Chang-Le Liu 劉昶樂, Po-Chun Hsu 許博竣, Feng-Ting Liao 廖峰挺, Yi-Chang Chen 陳宜昌, and the supervisor Da-Shan Shiu 許大山.* ## Features - Expanding the vocabulary dictionary for Traditional Chinese from 32k to 62k vocabulary size (the first successful work in Traditional Chinese) - Multi-turn dialogue without special handling for harmfulness - 8k context length - Grouped-query and sliding-window attention ## Model Details - **Finetuned from:** [MediaTek-Research/Breeze-7B-Base-v0.1](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0.1) - **Model type:** Causal decoder-only transformer language model - **Language:** English and Traditional Chinese (zh-tw) ## Performance | **Traditional Chinese Benchmarks:** | TMMLU+ (ACC) | DRCD (EM) | MT-Bench-tw (Score) | |-------------------------------------------------------------------------------------------------------|--------|------|-------------| | Breeze-7B-Base-v0.1 | | | | | Breeze-7B-Instruct-v0.1 | | | | | mistralai/Mistral-7B-v0.1 | | | | | mistralai/Mistral-7B-Instruct-v0.1 | | | | | yentinglin/Taiwan-LLM-7B-v2.1-base | | | | | yentinglin/Taiwan-LLM-7B-v2.1-chat | | | | | yentinglin/Taiwan-LLM-13B-v2.0-base | | | | | yentinglin/Taiwan-LLM-13B-v2.0-chat | | | | | 01-ai/Yi-6B-Base | | | | | 01-ai/Yi-6B-Chat | | | | | 01-ai/Yi-34B-Base | | | | | 01-ai/Yi-34B-Chat | | | | | Qwen/Qwen-7B | | | | | Qwen/Qwen-7B-Chat | | | | | Qwen/Qwen-14B | | | | | Qwen/Qwen-14B-Chat | | | | | gpt-3.5-turbo-0613 | | | | | **English Benchmarks:** | MMLU (ACC) | MT-Bench (Score) | |-------------------------------------------------------------------------------------------------------|--------|------| | Breeze-7B-Base-v0.1 | | | | Breeze-7B-Instruct-v0.1 | | | | mistralai/Mistral-7B-v0.1 | | | | mistralai/Mistral-7B-Instruct-v0.1 | | | | yentinglin/Taiwan-LLM-7B-v2.1-base | | | | yentinglin/Taiwan-LLM-7B-v2.1-chat | | | | yentinglin/Taiwan-LLM-13B-v2.0-base | | | | yentinglin/Taiwan-LLM-13B-v2.0-chat | | | | 01-ai/Yi-6B-Base | | | | 01-ai/Yi-6B-Chat | | | | 01-ai/Yi-34B-Base | | | | 01-ai/Yi-34B-Chat | | | | Qwen/Qwen-7B | | | | | Qwen/Qwen-7B-Chat | | | | | Qwen/Qwen-14B | | | | | Qwen/Qwen-14B-Chat | | | | | gpt-3.5-turbo-0613 | | | | **Inference Speed Test:** | Speed (char/sec) |-------------------------------------------------------------------------------------------------------|--------| | Breeze-7B-Instruct-v0.1 | | | mistralai/Mistral-7B-Instruct-v0.1 | | | yentinglin/Taiwan-LLM-7B-v2.1-chat | | | yentinglin/Taiwan-LLM-13B-v2.0-chat | | | 01-ai/Yi-6B-Chat | | | 01-ai/Yi-34B-Chat | | | Qwen/Qwen-7B-Chat | | | | | Qwen/Qwen-14B-Chat | | | | ## 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( model="MediaTek-Research/Breeze-7B-Instruct-v0.1", device_map="auto", torch_dtype=torch.bfloat16, use_flash_attn_2=True # optional ) ``` The structure of the query template follows that of Mistral-7B-Instruct, as shown below. ```txt SYS_PROMPT [INST] QUERY1 [/INST] RESPONSE1 [INST] QUERY2 [/INST] ``` where `SYS_PROMPT`, `QUERY1`, `RESPONSE1`, and `QUERY2` can be provided by the user. The suggested default `SYS_PROMPT` is ```txt You are a helpful AI assistant bulit by MediaTek Research. The user you helped speaks Traditional Chinese and comes from Taiwan. ```