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+ ---
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+ license: other
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+ license_name: seallms
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+ license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE
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+ language:
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+ - en
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+ - zh
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+ - vi
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+ - id
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+ - th
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+ - ms
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+ - km
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+ - lo
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+ - my
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+ - tl
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+ tags:
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+ - multilingual
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+ - sea
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+ ---
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+
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+ <p align="center">
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+ <img src="seal_logo.png" width="200" />
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+ </p>
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+
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+ # *SeaLLM-7B-v2* - Large Language Models for Southeast Asia
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+
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+ <p align="center">
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+ <a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2" target="_blank" rel="noopener"> 🤗 Tech Memo</a>
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+ &nbsp;&nbsp;
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+ <a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B" target="_blank" rel="noopener"> 🤗 DEMO</a>
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+ &nbsp;&nbsp;
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+ <a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a>
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+ &nbsp;&nbsp;
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+ <a href="https://arxiv.org/pdf/2312.00738.pdf" target="_blank" rel="noopener">Technical Report</a>
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+ </p>
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+
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+ We introduce [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2), the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since [SeaLLM-13B](https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat), with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc.
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+
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+ ### Highlights
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+ * [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves the **7B-SOTA** on the **GSM8K** task with **78.2** score and outperforms GPT-3.5 in many GSM8K-translated tasks in SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭) as well as MGSM (🇨🇳 🇹🇭). It also surpasses GPT-3.5 in MATH for Thai 🇹🇭.
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+ * It scores competitively against GPT-3.5 in many zero-shot commonsense benchmark, with **82.5, 68.3, 80.9** scores on Arc-C, Winogrande, and Hellaswag.
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+ * It achieves **7.54** score on the 🇬🇧 **MT-bench**, it ranks 3rd place on the leaderboard for 7B category and is the most outperforming multilingual model.
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+ * It scores **45.46** on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models ([Vistral-7B](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)) of similar sizes.
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+
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+
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+ ### Release and DEMO
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+
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+ - DEMO: [SeaLLMs/SeaLLM-7B](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B).
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+ - Technical report: [Arxiv: SeaLLMs - Large Language Models for Southeast Asia](https://arxiv.org/pdf/2312.00738.pdf).
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+ - Model weights: [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2).
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+
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+
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+ <blockquote style="color:red">
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+ <p><strong style="color: red">Terms of Use and License</strong>:
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+ By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b/edit/main/LICENSE" target="_blank" rel="noopener">SeaLLMs Terms Of Use</a>.
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+ </blockquote>
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+
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+ > **Disclaimer**:
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+ > We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation.
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+ > Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations.
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+ > In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.
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+
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+ > The logo was generated by DALL-E 3.
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+
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+
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+ ### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1?
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+
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+ * SeaLLM-7B-v2 is continue-pretrained from [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) and underwent carefully designed tuning with focus in reasoning.
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+
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+
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+ ## Evaluation
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+
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+
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+ ### Zero-shot Multilingual Math Reasoning
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+
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+ [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves with **78.2** score on the GSM8K, making it the **state of the art** in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with **22.4** vs 18.1 scores.
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+
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+ ![fig_sea_math_side_by_side.png](fig_sea_math_side_by_side.png)
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+
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+
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+ <details>
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+ <summary>See details on English and translated GSM8K and MATH</summary>
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+ <br>
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+
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+ | Model | GSM8K<br>en | MATH<br>en | GSM8K<br>zh | MATH<br>zh | GSM8K<br>vi | MATH<br>vi | GSM8K<br>id | MATH<br>id | GSM8K<br>th | MATH<br>th
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+ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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+ | GPT-3.5 | 80.8 | 34.1 | 48.2 | 21.5 | 55 | 26.5 | 64.3 | 26.4 | 35.8 | 18.1
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+ | Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6
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+ | Vistral-7b-chat | 48.2 | 12.5 | | | 48.7 | 3.1 | | | |
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+ | SeaLLM-7B-v2 | 78.2 | 27.5 | 53.7 | 17.6 | 69.9 | 23.8 | 71.5 | 24.4 | 59.6 | 22.4
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+
92
+ </details>
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+
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+ #### Zero-shot MGSM
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+
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+ [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th.
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+
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+ | Model | MGSM-Zh | MGSM-Th
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+ |-----| ----- | ---
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+ | ChatGPT (reported) | 61.2* | 47.2*
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+ | Qwen-14B-chat | 59.6 | 28
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+ | SeaLLM-7B-v2 | **64.8** | **62.4**
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+
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+
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+ ### Zero-shot Commonsense Reasoning
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+
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+ We compare [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) with ChatGPT and Mistral-7B-instruct on various zero-shot commonsense benchmarks (Arc-Challenge, Winogrande and Hellaswag). We use the 2-stage technique in [(Kojima et al., 2023)](https://arxiv.org/pdf/2205.11916.pdf) to grab the answer. Note that we **DID NOT** use "Let's think step-by-step" to invoke explicit CoT.
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+
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+ | Model | Arc-Challenge | Winogrande | Hellaswag
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+ |-----| ----- | --- | -- |
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+ | ChatGPT (reported) | 84.6* | 66.8* | 72.0*
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+ | ChatGPT (reproduced) | 84.1 | 63.1 | 79.5
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+ | Mistral-7B-Instruct | 68.1 | 56.4 | 45.6
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+ | SeaLLM-7B-v2 | 82.5 | 68.3 | 80.9
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+
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+
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+ ### Multilingual World Knowledge
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+
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+
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+ We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot [M3Exam](https://arxiv.org/pdf/2306.05179.pdf) (M3e) for En, Zh, Vi, Id, Th, and zero-shot [VMLU](https://vmlu.ai/) for Vi.
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+
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+ | Model | Langs | En<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>M3e
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+ |-----| ----- | --- | -- | ----- | ---- | --- | --- | --- |
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+ | ChatGPT | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41
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+ |-----| ----- | --- | -- | ----- | ---- | --- | --- | --- |
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+ | SeaLLM-13B | Multi | 52.78 | 62.69 | 44.50 | 46.45 | | 39.28 | 36.39
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+ | Vistral-7B | Mono | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27
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+ | SeaLLM-7B-v2 | Multi | 60.72 | 70.91 | 55.43 | 51.15 | 45.46 | 42.25 | 35.52
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+
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+
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+
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+ ### MT-Bench
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+
134
+ On the English [MT-bench](https://arxiv.org/abs/2306.05685) metric, SeaLLM-7B-v2 achieves **7.54** score on the MT-bench (3rd place on the leaderboard for 7B category), outperforms many 70B models and is arguably the only one that handles 10 SEA languages.
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+
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+ Refer to [mt_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/mt_bench/seallm_7b_v2.jsonl) for the MT-bench predictions of SeaLLM-7B-v2.
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+
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+ | Model | Access | Langs | MT-Bench
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+ | --- | --- | --- | --- |
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+ | GPT-4-turbo | closed | multi | 9.32
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+ | GPT-4-0613 | closed | multi | 9.18
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+ | Mixtral-8x7b (46B) | open | multi | 8.3
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+ | Starling-LM-7B-alpha | open | mono (en) | 8.0
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+ | OpenChat-3.5-7B | open | mono (en) | 7.81
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+ | **SeaLLM-7B-v2** | **open** | **multi (10+)** | **7.54**
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+ | [Qwen-14B](https://huggingface.co/Qwen/Qwen-14B-Chat) | open | multi | 6.96
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+ | [Llama-2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | open | mono (en) | 6.86
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+ | Mistral-7B-instuct | open | mono (en) | 6.84
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+
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+
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+ ### Sea-Bench
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+
153
+ Similar to MT-Bench, [Sea-bench](https://huggingface.co/datasets/SeaLLMs/Sea-bench) is a set of categorized instruction test sets to measure models' ability as an assistant that is specifically focused on 9 SEA languages, including non-Latin low-resource languages.
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+
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+ As shown, the huge improvements come from math-reasoning, reaching GPT-3.5 level of performance.
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+
157
+ ![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png)
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+
159
+ Refer to [sea_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/sea_bench/seallm_7b_v2.jsonl) for the Sea-bench predictions of SeaLLM-7B-v2.
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+
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+
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+
163
+ ### Usage
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+
165
+ #### Instruction format
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+
167
+ ```python
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+ prompt = """<|im_start|>system
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+ You are a helpful assistant.</s>
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+ <|im_start|>user
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+ Hello world</s>
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+ <|im_start|>assistant
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+ Hi there, how can I help?</s>
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+
175
+ # ! ENSURE 1 and only 1 bos `<s>` at the beginning of sequence
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+ print(tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt)))
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+
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+ ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁a', '▁helpful', '▁assistant', '.', '</s>', '▁', '<0x0A>', '<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁', '<0x0A>', '<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '?', '</s>', '▁', '<0x0A>']
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+ """
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+ ```
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+
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+ #### Using transformers's chat_template
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+ ```python
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+
185
+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
187
+ device = "cuda" # the device to load the model onto
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+
189
+ model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLM-7B-v2", torch_dtype=torch.bfloat16, device_map=device)
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+ tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM-7B-v2")
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+
192
+ messages = [
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+ {"role": "user", "content": "Hello world"},
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+ {"role": "assistant", "content": "Hi there, how can I help you today?"},
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+ {"role": "user", "content": "Explain general relativity in details."}
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+ ]
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+
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+ encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
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+ print(tokenizer.convert_ids_to_tokens(encodeds[0]))
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+ # ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁', '<0x0A>', '<', '|', 'im ....
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+
202
+ model_inputs = encodeds.to(device)
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+ model.to(device)
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+
205
+ generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.pad_token_id)
206
+ decoded = tokenizer.batch_decode(generated_ids)
207
+ print(decoded[0])
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+
209
+ ```
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+
211
+ #### Using vLLM
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+
213
+ ```python
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+ from vllm import LLM, SamplingParams
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+ TURN_TEMPLATE = "<|im_start|>{role}\n{content}</s>"
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+ TURN_PREFIX = "<|im_start|>{role}\n"
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+
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+ def seallm_chat_convo_format(conversations, add_assistant_prefix: bool, system_prompt=None):
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+ # conversations: list of dict with key `role` and `content` (openai format)
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+ if conversations[0]['role'] != 'system' and system_prompt is not None:
221
+ conversations = [{"role": "system", "content": system_prompt}] + conversations
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+ text = ''
223
+ for turn_id, turn in enumerate(conversations):
224
+ prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content'])
225
+ text += prompt
226
+ if add_assistant_prefix:
227
+ prompt = TURN_PREFIX.format(role='assistant')
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+ text += prompt
229
+ return text
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+
231
+ sparams = SamplingParams(temperature=0.1, max_tokens=1024, stop=['</s>', '<|im_start|>'])
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+ llm = LLM("SeaLLMs/SeaLLM-7B-v2", dtype="bfloat16")
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+
234
+ message = "Explain general relativity in details."
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+ prompt = seallm_chat_convo_format(message, True)
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+ gen = llm.generate(prompt, sampling_params)
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+
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+ print(gen[0].outputs[0].text)
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+ ```
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+
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+
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+ ## Acknowledgement to Our Linguists
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+
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+ We would like to express our special thanks to our professional and native linguists, Tantong Champaiboon, Nguyen Ngoc Yen Nhi and Tara Devina Putri, who helped build, evaluate, and fact-check our sampled pretraining and SFT dataset as well as evaluating our models across different aspects, especially safety.
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+
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+ ## Citation
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+
248
+ If you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: [[email protected]](mailto:[email protected])
249
+
250
+ **Author list and order will change!**
251
+
252
+ * `*` and `^` are equal contributions.
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+
254
+ ```
255
+ @article{damonlpsg2023seallm,
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+ author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*,
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+ Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang,
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+ Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang,
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+ Chaoqun Liu, Hang Zhang, Lidong Bing},
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+ title = {SeaLLMs - Large Language Models for Southeast Asia},
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+ year = 2023,
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+ Eprint = {arXiv:2312.00738},
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+ }
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+ ```
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+
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