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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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<p align="center"> |
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<img src="seal_logo.png" width="200" /> |
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</p> |
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# *SeaLLM-7B-v2* - Large Language Models for Southeast Asia |
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<p align="center"> |
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<a href="https://seallm.github.io" target="_blank" rel="noopener">Technical Blog</a> |
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<a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5" target="_blank" rel="noopener"> 🤗 Tech Memo</a> |
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<a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B" target="_blank" rel="noopener"> 🤗 DEMO</a> |
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<a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a> |
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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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We introduce [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5), 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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### Highlights |
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* [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5) outperforms GPT-3.5 and achieves 7B SOTA on most multilingual knowledge benchmarks for SEA languages (MMLU, M3Exam & VMLU). |
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* It achieves 79.0 and 34.9 on GSM8K and MATH, surpassing GPT-3.5 in MATH. |
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### Release and DEMO |
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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: |
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- [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2). |
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- [SeaLLM-7B-v2-GGUF](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF). |
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- Run locally: |
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- [LM-studio](https://lmstudio.ai/): |
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- [SeaLLM-7B-v2.5-q4_0-chatml](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF/blob/main/seallm-7b-v2.5-chatml.Q4_K_M.gguf) with ChatML template (`<eos>` token changed to `<|im_end|>`) |
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- [SeaLLM-7B-v2.5-q4_0](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF/blob/main/seallm-7b-v2.5.Q4_K_M.gguf) - must use SeaLLM-7B-v2.5 chat format. |
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- [MLX for Apple Silicon](https://github.com/ml-explore/mlx): [SeaLLMs/SeaLLM-7B-v2.5-mlx-quantized](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-mlx-quantized) |
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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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> **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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> The logo was generated by DALL-E 3. |
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### What's new since SeaLLM-7B-v2? |
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* SeaLLM-7B-v2.5 was built on top of Gemma-7b, and underwent large scale SFT and carefully designed alignment. |
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## Evaluation |
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### Multilingual World Knowledge |
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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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| 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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| GPT-3.5 | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41 |
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| Vistral-7B-chat | Mono | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27 |
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| Qwen1.5-7B-chat | Multi | 61.00 | 52.07 | 81.96 | 43.38 | 45.02 | 24.29 | 20.25 |
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| SailorLM | Multi | 52.72 | 59.76 | 67.74 | 50.14 | --- | 39.53 | 37.73 |
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| SeaLLM-7B-v2 | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52 |
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| SeaLLM-7B-v2.5 | Multi | 64.05 | 76.87 | 62.54 | 63.11 | 53.30 | 48.64 | 46.86 |
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### Zero-shot CoT Multilingual Math Reasoning |
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<!-- |
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[SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves with **78.5** score on the GSM8K with zero-shot CoT reasoning, 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 **28.4** vs 18.1 scores. |
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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 | 6.0 |
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| Vistral-7b-chat | 48.2 | 12.5 | | | 48.7 | 3.1 | | | | |
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| Qwen1.5-7B-chat | 56.8 | 15.3 | 40.0 | 2.7 | 37.7 | 9 | 36.9 | 7.7 | 21.9 | 4.7 |
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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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| SeaLLM-7B-v2.5 | 78.5 | 34.9 | 51.3 | 22.1 | 72.3 | 30.2 | 71.5 | 30.1 | 62.0 | 28.4 |
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Baselines were evaluated using their respective chat-template and system prompts ([Qwen1.5-7B-chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json), [Vistral](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)). |
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#### Zero-shot MGSM |
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[SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5) also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Thai. |
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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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| SeaLLM-7B-v2.5 | 58.0 | **64.8** |
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### Sea-Bench |
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### Usage |
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#### Instruction format |
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```python |
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prompt = """<|im_start|>system |
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You are a helpful assistant.<eos> |
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<|im_start|>user |
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Hello world<eos> |
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<|im_start|>assistant |
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Hi there, how can I help?<eos>""" |
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# <|im_start|> is not a special token. |
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# Transformers chat_template should be consistent with vLLM format below. |
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# ! 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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``` |
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#### Using transformers's chat_template |
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Install the latest transformers (>4.40) |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model onto |
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# use bfloat16 to ensure the best performance. |
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model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLM-7B-v2.5", torch_dtype=torch.bfloat16, device_map=device) |
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tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM-7B-v2.5") |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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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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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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model_inputs = encodeds.to(device) |
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model.to(device) |
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.pad_token_id) |
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decoded = tokenizer.batch_decode(generated_ids) |
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print(decoded[0]) |
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``` |
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#### Using vLLM |
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```python |
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from vllm import LLM, SamplingParams |
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TURN_TEMPLATE = "<|im_start|>{role}\n{content}<eos>\n" |
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TURN_PREFIX = "<|im_start|>{role}\n" |
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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: |
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conversations = [{"role": "system", "content": system_prompt}] + conversations |
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text = '' |
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for turn_id, turn in enumerate(conversations): |
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prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) |
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text += prompt |
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if add_assistant_prefix: |
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prompt = TURN_PREFIX.format(role='assistant') |
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text += prompt |
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return text |
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sparams = SamplingParams(temperature=0.1, max_tokens=1024, stop=['<eos>', '<|im_start|>']) |
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llm = LLM("SeaLLMs/SeaLLM-7B-v2.5", dtype="bfloat16") |
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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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print(gen[0].outputs[0].text) |
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``` |
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#### Fine-tuning SeaLLM-7B-v2.5 |
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Should follow the chat format and accurately mask out source tokens. Here is an example. |
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```python |
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conversations = [ |
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{"role": "system", "content": "You are helful assistant."}, |
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{"role": "user", "content": "Hello world."}, |
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{"role": "assistant", "content": "Hi there, how can I help?"}, |
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{"role": "user", "content": "Tell me a joke."}, |
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{"role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything."}, |
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] |
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def seallm_7b_v25_tokenize_multi_turns(tokenizer, conversations, add_assistant_prefix=False): |
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""" |
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Inputs: |
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conversations: list of dict following openai format, eg |
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conversations = [ |
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{"role": "system", "content": "You are helful assistant."}, |
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{"role": "user", "content": "Hello world."}, |
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{"role": "assistant", "content": "Hi there, how can I help?"}, |
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{"role": "user", "content": "Tell me a joke."}, |
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{"role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything."}, |
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] |
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add_assistant_prefix: whether to add assistant_prefix, only for inference decoding |
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Outputs: |
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tokenize_output_sample, { |
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"input_ids": ... |
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"token_type_ids": 1 if train and 0 if masked out (not train) |
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} |
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During training, need to create a labels, with masked-out tokens = -100 to avoid loss computations. |
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labels = sample['input_ids'].clone() |
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labels[sample['token_type_ids'] == 0] = -100 |
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""" |
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TURN_TEMPLATE = "<|im_start|>{role}\n{content}<eos>\n" |
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TURN_PREFIX = "<|im_start|>{role}\n" |
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sample = None |
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assistant_prefix_len = None |
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for turn_id, turn in enumerate(conversations): |
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prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) |
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turn_sample = tokenizer( |
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prompt, padding=False, truncation=False, verbose=False, add_special_tokens=False, |
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return_token_type_ids=True, |
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) |
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if turn['role'] == 'assistant': |
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if assistant_prefix_len is None: |
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assistant_prefix_len = len(tokenizer.encode(TURN_PREFIX.format(role=turn['role']), add_special_tokens=False)) |
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turn_sample['token_type_ids'][assistant_prefix_len:] = [1] * (len(turn_sample['input_ids']) - assistant_prefix_len) |
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if sample is None: |
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sample = turn_sample |
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else: |
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for k in turn_sample.keys(): |
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sample[k].extend(turn_sample[k]) |
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if add_assistant_prefix: |
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assistant_prefix_sample = tokenizer( |
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TURN_PREFIX.format(role="assistant"), padding=False, truncation=False, verbose=False, add_special_tokens=False, |
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return_token_type_ids=True, |
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) |
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for k in sample.keys(): |
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sample[k].extend(assistant_prefix_sample[k]) |
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if tokenizer.add_bos_token: |
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sample['input_ids'] = [tokenizer.bos_token_id] + sample['input_ids'] |
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sample['attention_mask'] = [1] + sample['attention_mask'] |
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sample['token_type_ids'] = [sample['token_type_ids'][0]] + sample['token_type_ids'] |
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return sample |
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# ! testing |
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sample = seallm_7b_v25_tokenize_multi_turns(tokenizer, conversations) |
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print(tokenizer.convert_ids_to_tokens(sample['input_ids'])) |
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print(sample['token_type_ids']) |
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``` |
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## Acknowledgement to Our Linguists |
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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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## Citation |
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If you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: [l.bing@alibaba-inc.com](mailto:l.bing@alibaba-inc.com) |
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**Author list and order will change!** |
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* `*` and `^` are equal contributions. |
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``` |
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@article{damonlpsg2023seallm, |
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author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Weiwen Xu, Hou Pong Chan, |
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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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