# *SeaLLM-7B-v2.5* - Large Language Models for Southeast Asia

Website 🤗 Tech Memo 🤗 DEMO Github Technical Report

# BIG NEWS: SeaLLM3 is released with state-of-the-art performance of diverse tasks while specifically enhanced to be more trustworthy. Please consider using that latest model version. 🔥[HOT] SeaLLMs project now has a dedicated website - [damo-nlp-sg.github.io/SeaLLMs](https://damo-nlp-sg.github.io/SeaLLMs/) 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. ### Highlights * [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). * It achieves 79.0 and 34.9 on GSM8K and MATH, surpassing GPT-3.5 in MATH. ### Release and DEMO - DEMO: - [SeaLLMs/SeaLLM-7B-v2.5](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B-v2.5). - [SeaLLMs/SeaLLM-7B | SeaLMMM-7B](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B) - Experimental multimodal SeaLLM. - Technical report: [Arxiv: SeaLLMs - Large Language Models for Southeast Asia](https://arxiv.org/pdf/2312.00738.pdf). - Model weights: - [SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5). - [SeaLLM-7B-v2.5-GGUF](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5-GGUF). - Run locally: - [LM-studio](https://lmstudio.ai/): - [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 (`` token changed to `<|im_end|>`) - [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. - [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) - Previous models: - [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) - [SeaLLM-7B-v1](https://huggingface.co/SeaLLMs/SeaLLM-7B-v1)

Terms of Use and License: By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our SeaLLMs Terms Of Use.

> **Disclaimer**: > 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. > 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. > 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. > The logo was generated by DALL-E 3. ### What's new since SeaLLM-7B-v2? * SeaLLM-7B-v2.5 was built on top of Gemma-7b, and underwent large scale SFT and carefully designed alignment. ## Evaluation ### Multilingual World Knowledge 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. | Model | Langs | En
MMLU | En
M3e | Zh
M3e | Vi
M3e | Vi
VMLU | Id
M3e | Th
M3e |-----| ----- | --- | -- | ----- | ---- | --- | --- | --- | | GPT-3.5 | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41 | Vistral-7B-chat | Mono | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27 | Qwen1.5-7B-chat | Multi | 61.00 | 52.07 | 81.96 | 43.38 | 45.02 | 24.29 | 20.25 | SailorLM | Multi | 52.72 | 59.76 | 67.74 | 50.14 | --- | 39.53 | 37.73 | SeaLLM-7B-v2 | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52 | SeaLLM-7B-v2.5 | Multi | 64.05 | 76.87 | 62.54 | 63.11 | 53.30 | 48.64 | 46.86 ### Zero-shot CoT Multilingual Math Reasoning | Model | GSM8K
en | MATH
en | GSM8K
zh | MATH
zh | GSM8K
vi | MATH
vi | GSM8K
id | MATH
id | GSM8K
th | MATH
th | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | GPT-3.5 | 80.8 | 34.1 | 48.2 | 21.5 | 55 | 26.5 | 64.3 | 26.4 | 35.8 | 18.1 | Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6.0 | Vistral-7b-chat | 48.2 | 12.5 | | | 48.7 | 3.1 | | | | | Qwen1.5-7B-chat | 56.8 | 15.3 | 40.0 | 2.7 | 37.7 | 9 | 36.9 | 7.7 | 21.9 | 4.7 | SeaLLM-7B-v2 | 78.2 | 27.5 | 53.7 | 17.6 | 69.9 | 23.8 | 71.5 | 24.4 | 59.6 | 22.4 | SeaLLM-7B-v2.5 | 78.5 | 34.9 | 51.3 | 22.1 | 72.3 | 30.2 | 71.5 | 30.1 | 62.0 | 28.4 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)). #### Zero-shot MGSM [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. | Model | MGSM-Zh | MGSM-Th |-----| ----- | --- | ChatGPT (reported) | 61.2 | 47.2 | Qwen-14B-chat | 59.6 | 28 | SeaLLM-7B-v2 | **64.8** | 62.4 | SeaLLM-7B-v2.5 | 58.0 | **64.8** ### Sea-Bench ![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png) ### Usage **IMPORTANT NOTICE for using the model** * `` must be at start of prompt, ff your code's tokenizer does not prepend `` by default, you MUST prepend into the prompt yourself, otherwise, it would not work! * Repitition penalty (e.g: in llama.cpp, ollama, LM-studio) must be set to **1** , otherwise will lead to degeneration! #### Instruction format ```python # ! WARNING, if your code's tokenizer does not prepend by default, # You MUST prepend into the prompt yourself, otherwise, it would not work! prompt = """<|im_start|>system You are a helpful assistant. <|im_start|>user Hello world <|im_start|>assistant Hi there, how can I help?""" # <|im_start|> is not a special token. # Transformers chat_template should be consistent with vLLM format below. # ! ENSURE 1 and only 1 bos `` at the beginning of sequence print(tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))) """ ``` #### Using transformers's chat_template Install the latest transformers (>4.40) ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # use bfloat16 to ensure the best performance. model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLM-7B-v2.5", torch_dtype=torch.bfloat16, device_map=device) tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM-7B-v2.5") messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world"}, {"role": "assistant", "content": "Hi there, how can I help you today?"}, {"role": "user", "content": "Explain general relativity in details."} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True) print(tokenizer.convert_ids_to_tokens(encodeds[0])) model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.pad_token_id) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` #### Using vLLM ```python from vllm import LLM, SamplingParams TURN_TEMPLATE = "<|im_start|>{role}\n{content}\n" TURN_PREFIX = "<|im_start|>{role}\n" def seallm_chat_convo_format(conversations, add_assistant_prefix: bool, system_prompt=None): # conversations: list of dict with key `role` and `content` (openai format) if conversations[0]['role'] != 'system' and system_prompt is not None: conversations = [{"role": "system", "content": system_prompt}] + conversations text = '' for turn_id, turn in enumerate(conversations): prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) text += prompt if add_assistant_prefix: prompt = TURN_PREFIX.format(role='assistant') text += prompt return text sparams = SamplingParams(temperature=0.1, max_tokens=1024, stop=['', '<|im_start|>']) llm = LLM("SeaLLMs/SeaLLM-7B-v2.5", dtype="bfloat16") message = "Explain general relativity in details." prompt = seallm_chat_convo_format(message, True) gen = llm.generate(prompt, sampling_params) print(gen[0].outputs[0].text) ``` #### Fine-tuning SeaLLM-7B-v2.5 Should follow the chat format and accurately mask out source tokens. Here is an example. ```python conversations = [ {"role": "system", "content": "You are helful assistant."}, {"role": "user", "content": "Hello world."}, {"role": "assistant", "content": "Hi there, how can I help?"}, {"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything."}, ] def seallm_7b_v25_tokenize_multi_turns(tokenizer, conversations, add_assistant_prefix=False): """ Inputs: conversations: list of dict following openai format, eg conversations = [ {"role": "system", "content": "You are helful assistant."}, {"role": "user", "content": "Hello world."}, {"role": "assistant", "content": "Hi there, how can I help?"}, {"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything."}, ] add_assistant_prefix: whether to add assistant_prefix, only for inference decoding Outputs: tokenize_output_sample, { "input_ids": ... "token_type_ids": 1 if train and 0 if masked out (not train) } During training, need to create a labels, with masked-out tokens = -100 to avoid loss computations. labels = sample['input_ids'].clone() labels[sample['token_type_ids'] == 0] = -100 """ TURN_TEMPLATE = "<|im_start|>{role}\n{content}\n" TURN_PREFIX = "<|im_start|>{role}\n" TURN_SUFFIX = "\n" TURN_SUFFIX_TAKE = "" sample = None assistant_prefix_len = None assistant_suffix_len = None for turn_id, turn in enumerate(conversations): prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) turn_sample = tokenizer( prompt, padding=False, truncation=False, verbose=False, add_special_tokens=False, return_token_type_ids=True, ) if turn['role'] == 'assistant': if assistant_prefix_len is None: assistant_prefix_len = len(tokenizer.encode(TURN_PREFIX.format(role=turn['role']), add_special_tokens=False)) if assistant_suffix_len is None: assistant_suffix_len = ( len(tokenizer.encode(TURN_SUFFIX.format(role=turn['role']), add_special_tokens=False)) - len(tokenizer.encode(TURN_SUFFIX_TAKE, add_special_tokens=False)) ) turn_sample['token_type_ids'][assistant_prefix_len:-assistant_suffix_len] = [1] * (len(turn_sample['input_ids']) - assistant_prefix_len - assistant_suffix_len) if sample is None: sample = turn_sample else: for k in turn_sample.keys(): sample[k].extend(turn_sample[k]) if add_assistant_prefix: assistant_prefix_sample = tokenizer( TURN_PREFIX.format(role="assistant"), padding=False, truncation=False, verbose=False, add_special_tokens=False, return_token_type_ids=True, ) for k in sample.keys(): sample[k].extend(assistant_prefix_sample[k]) if tokenizer.add_bos_token: sample['input_ids'] = [tokenizer.bos_token_id] + sample['input_ids'] sample['attention_mask'] = [1] + sample['attention_mask'] sample['token_type_ids'] = [sample['token_type_ids'][0]] + sample['token_type_ids'] return sample # ! testing sample = seallm_7b_v25_tokenize_multi_turns(tokenizer, conversations) tokens = tokenizer.convert_ids_to_tokens(sample['input_ids']) pairs = [(x, y) for x, y in zip(tokens, sample['token_type_ids'])] print(pairs) # source and special tokens is masked out (token_type 0), only assistant with is trained (token_type 1) # [('', 0), ('<', 0), ('|', 0), ..., ('assistant', 0), ('\n', 0), ('Hi', 1), ('▁there', 1), (',', 1), ('▁how', 1), ('▁can', 1), ('▁I', 1), ('▁help', 1), ('?', 1), ('', 1), ('\n', 0), ('<', 0), ... ``` ## Acknowledgement to Our Linguists 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. ## Citation 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) **Author list and order will change!** * `*` and `^` are equal contributions. ``` @article{damonlpsg2023seallm, author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Weiwen Xu, Hou Pong Chan, Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, title = {SeaLLMs - Large Language Models for Southeast Asia}, year = 2023, Eprint = {arXiv:2312.00738}, } ```