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README.md
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@@ -65,13 +65,13 @@ Here we compare Breeze-7B-Base-v1_0 with other open-source base language models
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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 |
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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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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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| 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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| 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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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 |
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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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## 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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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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| [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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| [**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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| [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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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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| [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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| [**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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| [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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| 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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| 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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| **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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| 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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| Details on TMMLU+ (0 shot):<br/>Model | STEM | Social Science | Humanities | Other | AVG |
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|-----------------------------------------------------|--------------|----------------|------------|------------|---------|
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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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| **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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| 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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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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| Qwen1.5-7B-Chat | 9.35 | 38.9k |
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| Yi-6B-Chat | 10.62 | 5.2k |
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| **Breeze-7B-Instruct-v1_0** | 10.74 | 11.1k |
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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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## 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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