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Model: MediaTek-Research/Breeze-7B-32k-Instruct-v1_0 Source: Original Platform
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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 MediaTek Research Breeze-7B-32k-Instruct-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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[Breeze-7B-32k-Base](https://huggingface.co/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.
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Roughly speaking, that is equivalent to 44k Traditional Chinese characters.
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[Breeze-7B-32k-Instruct](https://huggingface.co/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.
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Practicality-wise:
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- 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).]
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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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- Breeze-7B-32k-Instruct can perform tasks at a document level (For Chinese, 20 ~ 40 pages).
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*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.*
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## Features
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- Breeze-7B-32k-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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- 32k-token context length
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- Breeze-7B-32k-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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- 32k-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-32k-Base-v1_0
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- Pretrained from: [Breeze-7B-Base](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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- Breeze-7B-32k-Instruct-v1_0
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- Finetuned from: [Breeze-7B-32k-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-32k-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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## Long-context Performance
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#### Needle-in-a-haystack Performance
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We use the passkey retrieval task to test the model's ability to attend to different various depths in a given sequence.
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A key in placed within a long context distracting document for the model to retrieve.
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The key position is binned into 16 bins, and there are 20 testcases for each bin.
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Breeze-7B-32k-Base clears the tasks with 90+% accuracy, shown in the figure below.
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#### Long-DRCD Performance
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| **Model/Performance(EM)** | **DRCD** | **DRCD-16k** | **DRCD-32k** |
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|---------------------------|----------|--------------|--------------|
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| **Breeze-7B-32k-Instruct-v1\_0** | 76.9 | 54.82 | 44.26 |
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| **Breeze-7B-32k-Base-v1\_0** | 79.73 | 69.68 | 61.55 |
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| **Breeze-7B-Base-v1\_0** | 80.61 | 21.79 | 15.29 |
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#### Short-Benchmark Performance
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| **Model/Performance(EM)** | **TMMLU+** | **MMLU** | **TABLE** | **MT-Bench-tw** | **MT-Bench** |
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|---------------------------|----------|--------------|--------------|-----|-----|
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| **Breeze-7B-32k-Instruct-v1\_0** | 41.37 | 61.34 | 34 | 5.8 | 7.4 |
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| **Breeze-7B-Instruct-v1\_0** | 42.67 | 62.73 | 39.58 | 6.0 | 7.4 |
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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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<p style="color:red;">Flash-attention2 is strongly recommended for long context scenarios.</p>
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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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>>> tokenizer = AutoTokenizer.from_pretrained("MediaTek-Research/Breeze-7B-32k-Instruct-v1_0/")
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>>> model = AutoModelForCausalLM.from_pretrained(
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>>> "MediaTek-Research/Breeze-7B-32k-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"
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... )
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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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```
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## Citation
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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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"_name_or_path": "/nobackup/mtklmadm_t2/mtk53570/Breeze-650G-32k-step1500-hf/",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"output_router_logits": true,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": 65536,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.38.0",
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"use_cache": false,
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"vocab_size": 62464
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}
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60
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||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"61873": {
|
||||
"content": "<EOD>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"61874": {
|
||||
"content": "<PAD>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'].strip() %}{% else %}{% set loop_messages = messages %}{% set system_message = 'You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan.' %}{% endif %}{{ bos_token }} {{ system_message }} {% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/... or system/user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST] ' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "</s>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false,
|
||||
"use_fast": true
|
||||
}
|
||||
Reference in New Issue
Block a user