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Model: MediaTek-Research/Breeze-7B-Instruct-v0_1
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README.md

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+---
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- zh
+- en
+---
+
+# Model Card for MediaTek Research Breeze-7B-Instruct-v0_1
+
+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.
+
+[Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0_1) 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://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1) derives from the base model Breeze-7B-Base, making the resulting model amenable to be used as-is for commonly seen tasks.
+
+[Breeze-7B-Instruct-64k](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0_1) is a slightly modified version of 
+Breeze-7B-Instruct to enable a 64k-token context length. Roughly speaking, that is equivalent to 88k Traditional Chinese characters.
+
+*Update (Feb. 21st, 2024): Breeze-7B-Instruct-64k-v0_1 has been temporarily removed from Hugging Face due to its actual performance in long context tests not meeting expectations.*
+
+*Update (Mar. 7th, 2024): The current release version of Breeze-7B is v1.0. See [Breeze-7B-Instruct-v1_0](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0).*
+
+The current release version of Breeze-7B is v0.1.
+
+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.
+- In particular, Breeze-7B-Instruct-64k can perform tasks at a document level, not a chapter level.
+
+
+Performance-wise:
+- 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-7B-Chat, and Yi-6B-Chat. [See [Chat Model Performance](#chat-model-performance).]
+
+
+*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.*
+
+## Features
+
+- Breeze-7B-Base-v0_1
+  - Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
+  - 8k-token context length
+- Breeze-7B-Instruct-v0_1
+  - Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese 
+  - 8k-token context length
+  - Multi-turn dialogue (without special handling for harmfulness)
+- Breeze-7B-Instruct-64k-v0_1
+  - Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
+  - 64k-token context length
+  - Multi-turn dialogue (without special handling for harmfulness)
+
+## Model Details
+
+- Breeze-7B-Base-v0_1
+  - Finetuned from: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+  - Model type: Causal decoder-only transformer language model
+  - Language: English and Traditional Chinese (zh-tw)
+- Breeze-7B-Instruct-v0_1
+  - 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)
+- Breeze-7B-Instruct-64k-v0_1
+  - Finetuned from: [MediaTek-Research/Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1)
+  - Model type: Causal decoder-only transformer language model
+  - Language: English and Traditional Chinese (zh-tw)
+
+## Base Model Performance
+
+**TMMLU+**, **DRCD**, and **Table** source from [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2).
+[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)
+ and [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus). **MMLU** sources from [hails/mmlu_no_train](https://huggingface.co/datasets/hails/mmlu_no_train).
+ 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.
+
+
+| Models                                       |        |↑ TMMLU+ (ACC) | DRCD (EM)   | Table (ACC) | MMLU (ACC) |
+|----------------------------------------------|--------|--------------|-------------|-------------|------------|
+|                                              |        |TC, Knowledge |TC, Reasoning|TC, Reasoning|EN, Knowledge|
+|                                              |        | 5 shot       | 3 shot      | 5 shot      | 5 shot     |
+| [Yi-34B](https://huggingface.co/01-ai/Yi-34B)| 34B    | 63.10        | 84.57       | 49.31  | 77.42      |
+| [Qwen-14B](https://huggingface.co/01-ai/Qwen/Qwen-14B)| 14B    | 51.30        | 16.95 *     | 50.69  | 68.83      |
+| [Yi-6B](https://huggingface.co/01-ai/Yi-6B) | 6B     | 49.63        | 76.61       | 34.72  | 65.35      |
+| [Qwen-7B](https://huggingface.co/01-ai/Qwen/Qwen-7B)| 7B     | 42.84        | 0.0 *       | 39.58  | 61.00      |
+| [**Breeze-7B-Base-v0_1**](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0_1)       | 7B     | 40.35        | 81.13        | 28.47  | 61.63      |
+| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)| 7B     | 36.93        | 79.27        | 27.78 | 64.89      |
+
+
+\* Few-shot learning cannot effectively guide the model to generate the proper answer.
+
+
+## Chat Model Performance
+
+**TMMLU+**, **DRCD**, **Table**, and **MT-Bench-tw** source from [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2).
+[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)
+ and [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus). **MMLU** sources from [hails/mmlu_no_train](https://huggingface.co/datasets/hails/mmlu_no_train).
+ **MT-Bench** source from [lmsys/mt_bench_human_judgments](https://huggingface.co/datasets/lmsys/mt_bench_human_judgments).
+ 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.
+ 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**.
+
+
+| Models                                                                                                  |        |↑ MT-Bench-tw (Score)| TMMLU+ (ACC) | TMMLU+ (ACC) | DRCD (EM)   | Table (ACC) | MT-Bench (Score) | MMLU (ACC)  | MMLU (ACC)  | 
+|---------------------------------------------------------------------------------------------------------|--------|--------------------|--------------|--------------|-------------|-------------|------------------|-------------|-------------|
+|                                                                                                         |        |TC, Chat            |TC, Knowledge |TC, Knowledge |TC, Reasoning|TC, Reasoning|EN, Chat          |EN, Knowledge|EN, Knowledge|
+|                                                                                                         |        |0 shot              | 0 shot       | 5 shot       | 3 shot      | 0 shot      |0 shot            |  0 shot     | 5 shot      | 
+| [gpt-3.5-turbo](https://openai.com)                                                                     |        |7.1                 | 43.56        |              |             | 45.14       |7.9               |  67.09      |             |    
+| [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat)                                                 | 34B    |6.9                 | 54.87        |              |             | 36.81       |7.6               |   71.04     |             |    
+| [Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat)                                              | 14B    |6.4                 | 48.41        |              |             | 41.67       |7.2               |    64.91    |             |    
+| [**Breeze-7B-Instruct-v0_1**](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1)         | 7B     |5.7                 | 41.61        |              |             | 45.83       |7.1               |    63.26    |             |    
+| [**Breeze-7B-Instruct-64k-v0_1**](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0_1) | 7B     |5.5                 | 40.99        |              |             | 36.11       |7.1               |    63.68    |             |    
+| [Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat)                                                | 7B     |5.4                 | 40.02        |              |             | 33.33       |6.2               |    55.94    |             |    
+| [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat)                                                   | 6B     |5.0                 | 44.79        |              |             | 25.69       |6.0               |    59.45    |             |    
+| [Taiwan-LLM-13B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat)                  | 13B    |5.0                 | 29.47        |              |             | 23.61       |-*                |    50.50    |             |     
+| [Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat)                    | 7B     |4.2                 | 28.08        |              |             | 31.25       | -*               |    42.72    |             |    
+
+\* Taiwan-LLM models responds to multi-turn questions (English) in Traditional Chinese.
+
+
+| Details on MT-Bench-tw (0 shot):<br/>Models         | STEM    |Extraction|Reasoning| Math   | Coding  | Roleplay| Writing |Humanities|↑ AVG   |
+|-----------------------------------------------------|---------|---------|---------|---------|---------|---------|---------|---------|---------|
+| gpt-3.5-turbo                                       |  7.8    |  6.1    |   5.1   |   6.4   |  6.2    |   8.7   |   7.4   |   9.3   |   7.1   |
+| Yi-34B-Chat                                         |  9.0    |  4.8    |   5.7   |   4.0   |  4.7    |   8.5   |   8.7   |   9.8   |   6.9   |
+| Qwen-14B-Chat                                       |  7.6    |  5.7    |   4.5   |   4.2   |  5.3    |   7.5   |   7.3   |   9.1   |   6.4   |
+| **Breeze-7B-Instruct-v0_1**                         |  6.5    |  5.6    |   3.9   |   3.6   |  4.3    |   6.9   |   5.7   |   9.3   |   5.7   |
+| **Breeze-7B-Instruct-64k-v0_1**                     |  6.1    |  5.3    |   3.7   |   2.9   |  4.2    |   7.0   |   6.7   |   8.3   |   5.5   |
+| Qwen-7B-Chat                                        |  6.6    |  4.5    |   4.8   |   2.9   |  3.6    |   6.2   |   6.8   |   8.2   |   5.4   |
+| Yi-6B-Chat                                          |  7.3    |  2.7    |   3.1   |   3.3   |  2.3    |   7.2   |   5.2   |   8.8   |   5.0   |
+| Taiwan-LLM-13B-v2.0-chat                            |  6.1    |  3.4    |   4.1   |   2.3   |  3.1    |   7.4   |   6.6   |   6.8   |   5.0   |
+| Taiwan-LLM-7B-v2.1-chat                             |  5.2    |  2.6    |   2.3   |   1.2   |  3.4    |   6.6   |   5.7   |   6.8   |   4.2   |
+
+
+| Details on TMMLU+ (0 shot):<br/>Model               | STEM         | Social Science | Humanities | Other      | ↑ AVG   |
+|-----------------------------------------------------|--------------|----------------|------------|------------|---------|
+| Yi-34B-Chat                                         | 47.65        | 64.25          | 52.73      | 54.91      | 54.87   |
+| Qwen-14B-Chat                                       | 43.83        | 55.00          | 48.55      | 46.22      | 48.41   |
+| Yi-6B-Chat                                          | 37.80        | 51.74          | 45.36      | 44.25      | 44.79   |
+| gpt-3.5-turbo                                       | 41.58        | 48.52          | 40.96      | 43.18      | 43.56   |
+| **Breeze-7B-Instruct-v0_1**                         | 37.41        | 46.81          | 42.06      | 40.16      | 41.61   |
+| **Breeze-7B-Instruct-64k-v0_1**                     | 37.88        | 46.35          | 40.31      | 39.40      | 40.99   |
+| Qwen-7B-Chat                                        | 35.44        | 46.22          | 38.35      | 40.06      | 40.02   |
+| Taiwan-LLM-13B-v2.0-chat                            | 27.74        | 33.69          | 27.03      | 29.43      | 29.47   |
+| Taiwan-LLM-7B-v2.1-chat                             | 25.58        | 31.76          | 27.36      | 27.61      | 28.08   |
+
+
+
+## Inference Performance
+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.
+All inferences run on 2 RTX A6000 GPUs (using `vllm`, with a tensor-parallel size of 2).
+
+| Models                                                             | ↓ Inference Time (sec)|Estimated Max Input Length (Char)|
+|--------------------------------------------------------------------|-------------------|--------------------------|
+| Yi-6B-Chat                                                         |   10.62  |   5.2k                |
+| **Breeze-7B-Instruct-v0_1**                                        |  10.74  |    11.1k                 |
+| **Breeze-7B-Instruct-64k-v0_1**                                    | 10.74       |  88.8k            |
+| Qwen-7B-Chat                                                       |   10.86         |    9.8k                  |
+| Qwen-14B-Chat                                                      |   18.89  |    9.8k                  |
+| Mistral-7B-v0.1-Instruct                                           |  20.48   |    5.1k                 |
+| Taiwan-LLM-7B-v2.1-chat                                            |   26.26          |    2.2k                  |
+| Taiwan-LLM-13B-v2.0-chat                                           |   36.80          |    2.2k                  |
+| Yi-34B-Chat                                                        |  43.71   |    4.5k                  |
+
+## Long-context Performance
+
+TBD
+
+## 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(
+    "MediaTek-Research/Breeze-7B-Instruct-v0_1",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2" # optional
+)
+```
+
+The structure of the query is 
+```txt
+<s>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 built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan.
+```
+
+We also integrate `chat_template` into [tokenizer_config.json](tokenizer_config.json), so you can `apply_chat_template` to get the prompt.
+
+```python
+>>> from transformers import AutoTokenizer
+>>> tokenizer = AutoTokenizer.from_pretrained("MediaTek-Research/Breeze-7B-Instruct-v0_1")
+>>> 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}
+}
+```

added_tokens.json

@@ -0,0 +1,4 @@
+{
+  "<EOD>": 61873,
+  "<PAD>": 61874
+}

config.json

@@ -0,0 +1,26 @@
+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.1",
+  "use_cache": false,
+  "vocab_size": 61952
+}

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.34.1"
+}

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model.safetensors.index.json

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special_tokens_map.json

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+{
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+}

tokenizer.model

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+size 911034

tokenizer_config.json

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+  "use_default_system_prompt": false
+}