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Quantization made by Richard Erkhov. |
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[Github](https://github.com/RichardErkhov) |
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[Discord](https://discord.gg/pvy7H8DZMG) |
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[Request more models](https://github.com/RichardErkhov/quant_request) |
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Breeze-7B-Base-v1_0 - bnb 4bits |
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- Model creator: https://huggingface.co/MediaTek-Research/ |
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- Original model: https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v1_0/ |
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Original model description: |
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--- |
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pipeline_tag: text-generation |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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--- |
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# Model Card for Breeze-7B-Base-v1_0 |
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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. |
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[Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v1_0) is the base model for the Breeze-7B series. |
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It is suitable for use if you have substantial fine-tuning data to tune it for your specific use case. |
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[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. |
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The current release version of Breeze-7B is v1.0, which has undergone a more refined training process compared to Breeze-7B-v0_1, resulting in significantly improved performance in both English and Traditional Chinese. |
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For details of this model please read our [paper](https://arxiv.org/abs/2403.02712). |
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Practicality-wise: |
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- Breeze-7B-Base expands the original vocabulary with an additional 30,000 Traditional Chinese tokens. With the expanded vocabulary, and 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).] |
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- Breeze-7B-Instruct can be used as is for common tasks such as Q&A, RAG, multi-round chat, and summarization. |
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Performance-wise: |
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- Breeze-7B-Instruct demonstrates impressive performance in benchmarks for Traditional Chinese and English when compared to similar-sized open-source contemporaries such as Taiwan-LLM-7B/13B-chat, QWen(1.5)-7B-Chat, and Yi-6B-Chat. [See [Chat Model Performance](#chat-model-performance).] |
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*A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Chang-Le Liu 劉昶樂, 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.* |
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## Demo |
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[Try Demo Here](https://huggingface.co/spaces/MediaTek-Research/Demo_Breeze-7B-Instruct-v1.0) |
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## Features |
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- Breeze-7B-Base-v1_0 |
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- Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese |
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- 8k-token context length |
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- Breeze-7B-Instruct-v1_0 |
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- Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese |
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- 8k-token context length |
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- Multi-turn dialogue (without special handling for harmfulness) |
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## Model Details |
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- Breeze-7B-Base-v1_0 |
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- Finetuned from: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) |
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- Model type: Causal decoder-only transformer language model |
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- Language: English and Traditional Chinese (zh-tw) |
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- Breeze-7B-Instruct-v1_0 |
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- Finetuned from: [MediaTek-Research/Breeze-7B-Base-v1_0](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v1_0) |
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- Model type: Causal decoder-only transformer language model |
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- Language: English and Traditional Chinese (zh-tw) |
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## Base Model Performance |
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Here we compare Breeze-7B-Base-v1_0 with other open-source base language models of similar parameter size that are widely recognized for their good performance in Chinese. |
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**TMMLU+**, **DRCD**, and **Table** source from [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2). |
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[MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2) derives from [TCEval-v1](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval) |
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and [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus). **MMLU** sources from [hails/mmlu_no_train](https://huggingface.co/datasets/hails/mmlu_no_train). |
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We use the code revised from [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate **TMMLU+**, **DRCD**, **Table**, and **MMLU**. All choice problems adapt the selection by the log-likelihood. |
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| Models | #Parameters | TMMLU+ (ACC) | DRCD (EM) | Table (ACC) | MMLU (ACC) | |
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|---------------------------------------------- |--------|--------------|-------------|-------------|------------| |
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| | |TC, Knowledge |TC, Reasoning|TC, Reasoning|EN, Knowledge| |
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| | | 5 shot | 3 shot | 5 shot | 5 shot | |
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| [**Breeze-7B-Base-v1_0**](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v1_0) | 7B | 42.67 | 80.61 | 31.99 | 61.24 | |
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| [Yi-6B](https://huggingface.co/01-ai/Yi-6B) | 6B | 49.63 | 76.61 | 34.72 | 65.35 | |
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| [Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) | 7B | 46.59 | 74.41 | 30.56 | 63.07 | |
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| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 7B | 36.93 | 79.27 | 27.78 | 64.89 | |
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## Instruction-tuned Model Performance |
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Here we compare Breeze-7B-Instruct-v1_0 with other open-source instruction-tuned language models of similar parameter size that are widely recognized for their good performance in Chinese. |
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Also, we listed the benchmark scores of GPT-3.5 Turbo (1106), which represents one of the most widely used high-quality cloud language model API services, for reference. |
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**TMMLU+**, **DRCD**, **Table**, and **MT-Bench-tw** source from [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2). |
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[MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2) derives from [TCEval-v1](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval) |
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and [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus). **MMLU** sources from [hails/mmlu_no_train](https://huggingface.co/datasets/hails/mmlu_no_train). |
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**MT-Bench** source from [lmsys/mt_bench_human_judgments](https://huggingface.co/datasets/lmsys/mt_bench_human_judgments). |
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We use the code revised from [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate **TMMLU+**, **DRCD**, **Table**, and **MMLU**. All choice problems adapt the selection by the log-likelihood. |
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We use the code revised from [fastchat llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) (GPT4 as judge) to evaluate **MT-Bench-tw** and **MT-Bench**. |
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| Models | #Parameters | MT-Bench-tw (Score)| TMMLU+ (ACC) | Table (ACC) | MT-Bench (Score) | MMLU (ACC) | |
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|---------------------------------------------------------------------------------------------------------|--------|--------------------|--------------|-------------|------------------|-------------| |
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| | |TC, Chat |TC, Knowledge |TC, Reasoning|EN, Chat |EN, Knowledge| |
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| | |0 shot | 0 shot | 0 shot |0 shot | 0 shot | |
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| [**Breeze-7B-Instruct-v1_0**](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0) | 7B |6.0 | 42.67 | 39.58 |7.4 | 61.73 | |
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| [GPT-3.5-Turbo](https://openai.com) | |7.1 | 43.56 | 45.14 |7.9 | 67.09 | |
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| [Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) | 7B |6.4 | 45.65 | 34.72 |7.6 | 61.85 | |
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| [Mistral-7B-v0.2-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 7B |5.6 | 34.95 | 33.33 |7.6 | 59.97 | |
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| [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) | 6B |5.0 | 44.79 | 25.69 |6.0 | 59.45 | |
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| [Taiwan-LLM-13B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat) | 13B |5.0 | 29.47 | 23.61 |N/A* | 50.50 | |
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| [Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) | 7B |4.2 | 28.08 | 31.25 |N/A* | 42.72 | |
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\* Taiwan-LLM models respond to multi-turn questions (English) in Traditional Chinese. |
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| Details on MT-Bench-tw (0 shot):<br/>Models | STEM |Extraction|Reasoning| Math | Coding | Roleplay| Writing |Humanities| AVG | |
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|-----------------------------------------------------|---------|---------|---------|---------|---------|---------|---------|----------| --------- | |
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| **Breeze-7B-Instruct-v1_0** | 7.8 | 5.2 | 4.2 | 4.2 | 4.1 | 7.6 | 5.9 | 9.1 | 6.0 | |
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| GPT-3.5-Turbo | 7.8 | 6.1 | 5.1 | 6.4 | 6.2 | 8.7 | 7.4 | 9.3 | 7.1 | |
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| Qwen1.5-7B-Chat | 9 | 5.6 | 4.7 | 2.8 | 3.7 | 8.0 | 8.0 | 9.4 | 6.4 | |
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| Mistral-7B-v0.2-Instruct | 6.9 | 4.6 | 4.3 | 3.3 | 4.4 | 7.2 | 6.2 | 7.8 | 5.6 | |
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| Yi-6B-Chat | 7.3 | 2.7 | 3.1 | 3.3 | 2.3 | 7.2 | 5.2 | 8.8 | 5.0 | |
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| Taiwan-LLM-13B-v2.0-chat | 6.1 | 3.4 | 4.1 | 2.3 | 3.1 | 7.4 | 6.6 | 6.8 | 5.0 | |
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| Taiwan-LLM-7B-v2.1-chat | 5.2 | 2.6 | 2.3 | 1.2 | 3.4 | 6.6 | 5.7 | 6.8 | 4.2 | |
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| Details on TMMLU+ (0 shot):<br/>Model | STEM | Social Science | Humanities | Other | AVG | |
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|-----------------------------------------------------|--------------|----------------|------------|------------|---------| |
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| **Breeze-7B-Instruct-v1_0** | 36.46 | 48.38 | 45.11 | 40.75 | 42.67 | |
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| Mistral-7B-v0.2-Instruct | 32.79 | 38.05 | 34.89 | 34.04 | 34.94 | |
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| Yi-6B-Chat | 37.80 | 51.74 | 45.36 | 44.25 | 44.79 | |
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| GPT-3.5-Turbo | 41.58 | 48.52 | 40.96 | 43.18 | 43.56 | |
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| Qwen1.5-7B-Chat | 41.48 | 51.66 | 44.05 | 45.40 | 45.65 | |
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| Taiwan-LLM-13B-v2.0-chat | 27.74 | 33.69 | 27.03 | 29.43 | 29.47 | |
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| Taiwan-LLM-7B-v2.1-chat | 25.58 | 31.76 | 27.36 | 27.61 | 28.08 | |
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## Inference Performance |
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In this test, we use the first 700 characters of the [web article](https://health.udn.com/health/story/5976/7699252?from=udn_ch1005_main_index) as the input and ask the model to write the same article again. |
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All inferences run on 2 RTX A6000 GPUs (using `vllm`, with a tensor-parallel size of 2). |
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| Models | Inference Time (sec)|Estimated Max Input Length (Char)| |
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|--------------------------------------------------------------------|-------------------|--------------------------| |
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| **Breeze-7B-Instruct-v1_0** | 10.74 | 11.1k | |
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| Qwen1.5-7B-Chat | 9.35 | 38.9k | |
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| Yi-6B-Chat | 10.62 | 5.2k | |
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| Mistral-7B-Instruct-v0.2 | 20.48 | 5.1k | |
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| Taiwan-LLM-7B-v2.1-chat | 26.26 | 2.2k | |
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<!---| Taiwan-LLM-13B-v2.0-chat | 36.80 | 2.2k |---> |
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<!---## Long-context Performance |
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TBD---> |
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## Use in Transformers |
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First install direct dependencies: |
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``` |
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pip install transformers torch accelerate |
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``` |
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If you want faster inference using flash-attention2, you need to install these dependencies: |
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```bash |
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pip install packaging ninja |
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pip install flash-attn |
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``` |
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Then load the model in transformers: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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# Instruction Model |
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model = AutoModelForCausalLM.from_pretrained( |
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"MediaTek-Research/Breeze-7B-Instruct-v1_0", |
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device_map="auto", |
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torch_dtype=torch.bfloat16, |
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# attn_implementation="flash_attention_2" # optional |
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) |
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# Basemodel |
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model = AutoModelForCausalLM.from_pretrained( |
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"MediaTek-Research/Breeze-7B-Base-v1_0", |
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device_map="auto", |
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torch_dtype=torch.bfloat16, |
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# attn_implementation="flash_attention_2" # optional |
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) |
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``` |
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**For Breeze-7B-Instruct**, the structure of the query is |
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```txt |
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<s>SYS_PROMPT [INST] QUERY1 [/INST] RESPONSE1 [INST] QUERY2 [/INST] |
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``` |
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where `SYS_PROMPT`, `QUERY1`, `RESPONSE1`, and `QUERY2` can be provided by the user. |
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The suggested default `SYS_PROMPT` is |
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```txt |
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You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan. |
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``` |
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We also integrate `chat_template` into [tokenizer_config.json](tokenizer_config.json), so you can `apply_chat_template` to get the prompt. |
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```python |
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>>> from transformers import AutoTokenizer |
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>>> tokenizer = AutoTokenizer.from_pretrained("MediaTek-Research/Breeze-7B-Instruct-v1_0") |
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>>> chat = [ |
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... {"role": "user", "content": "你好,請問你可以完成什麼任務?"}, |
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... {"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。"}, |
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... {"role": "user", "content": "太棒了!"}, |
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... ] |
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>>> tokenizer.apply_chat_template(chat, tokenize=False) |
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"<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] " |
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# Tokenized results |
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# ['▁', '你好', ',', '請問', '你', '可以', '完成', '什麼', '任務', '?'] |
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# ['▁', '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', '。'] |
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# ['▁', '太', '棒', '了', '!'] |
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>>> outputs = model.generate(tokenizer.apply_chat_template(chat, return_tensors="pt"), max_new_tokens=128) |
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>>> print(tokenizer.decode(outputs[0])) |
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``` |
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## Citation |
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<!-- |
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``` |
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@article{breeze7b2024, |
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title={}, |
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author={}, |
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journal={arXiv}, |
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year={2024} |
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} |
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``` |
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---> |
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``` |
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@article{MediaTek-Research2024breeze7b, |
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title={Breeze-7B Technical Report}, |
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author={Chan-Jan Hsu and Chang-Le Liu and Feng-Ting Liao and Po-Chun Hsu and Yi-Chang Chen and Da-Shan Shiu}, |
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year={2024}, |
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eprint={2403.02712}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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