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SeaLLM-7B-v2 - GGUF

Name Quant method Size
SeaLLM-7B-v2.Q2_K.gguf Q2_K 2.6GB
SeaLLM-7B-v2.IQ3_XS.gguf IQ3_XS 2.89GB
SeaLLM-7B-v2.IQ3_S.gguf IQ3_S 3.04GB
SeaLLM-7B-v2.Q3_K_S.gguf Q3_K_S 3.03GB
SeaLLM-7B-v2.IQ3_M.gguf IQ3_M 3.14GB
SeaLLM-7B-v2.Q3_K.gguf Q3_K 3.36GB
SeaLLM-7B-v2.Q3_K_M.gguf Q3_K_M 3.36GB
SeaLLM-7B-v2.Q3_K_L.gguf Q3_K_L 3.64GB
SeaLLM-7B-v2.IQ4_XS.gguf IQ4_XS 3.76GB
SeaLLM-7B-v2.Q4_0.gguf Q4_0 3.91GB
SeaLLM-7B-v2.IQ4_NL.gguf IQ4_NL 3.96GB
SeaLLM-7B-v2.Q4_K_S.gguf Q4_K_S 3.94GB
SeaLLM-7B-v2.Q4_K.gguf Q4_K 4.16GB
SeaLLM-7B-v2.Q4_K_M.gguf Q4_K_M 4.16GB
SeaLLM-7B-v2.Q4_1.gguf Q4_1 4.33GB
SeaLLM-7B-v2.Q5_0.gguf Q5_0 4.75GB
SeaLLM-7B-v2.Q5_K_S.gguf Q5_K_S 4.75GB
SeaLLM-7B-v2.Q5_K.gguf Q5_K 4.87GB
SeaLLM-7B-v2.Q5_K_M.gguf Q5_K_M 4.87GB
SeaLLM-7B-v2.Q5_1.gguf Q5_1 5.17GB
SeaLLM-7B-v2.Q6_K.gguf Q6_K 5.64GB

Original model description:

license: other license_name: seallms license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE language: - en - zh - vi - id - th - ms - km - lo - my - tl tags: - multilingual - sea

SeaLLM-7B-v2 - Large Language Models for Southeast Asia

BIG NEWS: SeaLLM-7B-v2.5 is released with state-of-the-art performance in world knowledge and reasoning. SeaLLM-7B-v2 will begin deprecation.

Technical Blog    🤗 Tech Memo    🤗 DEMO    Github    Technical Report

We introduce SeaLLM-7B-v2, the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since SeaLLM-13B, with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc.

Highlights

  • SeaLLM-7B-v2 achieves the 7B-SOTA on the Zero-shot CoT 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 CoT for Thai 🇹🇭.
  • It scores competitively against GPT-3.5 in many zero-shot CoT commonsense benchmark, with 82.5, 68.3, 80.9 scores on Arc-C, Winogrande, and Hellaswag.
  • 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.
  • It scores 45.74 on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models (Vistral-7B) of similar sizes.

Release and DEMO

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-13B-v1 and SeaLLM-7B-v1?

  • SeaLLM-7B-v2 is continue-pretrained from Mistral-7B and underwent carefully designed tuning with focus in reasoning.

Evaluation

Zero-shot CoT Multilingual Math Reasoning

SeaLLM-7B-v2 achieves with 78.2 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 also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with 22.4 vs 18.1 scores.

fig_sea_math_side_by_side.png

See details on English and translated GSM8K and MATH with zero-shot 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
Vistral-7b-chat 48.2 12.5 48.7 3.1
Qwen1.5-7B-chat 56.8 15.3 40 2.7 37.7 9 36.9 7.7 21.9
SeaLLM-7B-v2 78.2 27.5 53.7 17.6 69.9 23.8 71.5 24.4 59.6 22.4

Baselines were evaluated using their respective chat-template and system prompts (Qwen1.5-7B-chat, Vistral).

Zero-shot MGSM

SeaLLM-7B-v2 also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th.

Model MGSM-Zh MGSM-Th
ChatGPT (reported) 61.2 47.2
Qwen-14B-chat 59.6 28
SeaLLM-7B-v2 64.8 62.4

Zero-shot Commonsense Reasoning

We compare 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) to grab the answer. Note that we DID NOT use "Let's think step-by-step" to invoke explicit CoT.

0-shot reasoning Arc-Challenge Winogrande Hellaswag
ChatGPT (reported) 84.6* 66.8* 72.0*
ChatGPT (reproduced) 84.1 63.1 79.5
Mistral-7B-Instruct 68.1 56.4 45.6
Qwen1.5-7B-chat 79.3 59.4 69.3
SeaLLM-7B-v2 82.5 68.3 80.9

Baselines were evaluated using their respective chat-template and system prompts (Qwen1.5-7B-chat, Mistral).

Multilingual World Knowledge

We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot M3Exam (M3e) for En, Zh, Vi, Id, Th, and zero-shot VMLU 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
SeaLLM-7B-v2 Multi 61.89 70.91 55.43 51.15 45.74 42.25 35.52

VMLU reproduce script here. Lm-eval was used to evaluate MMLU. 0-shot VMLU scores for baselines were evaluated using their respective chat-template and system prompts (Qwen1.5-7B-chat).

MT-Bench

On the English MT-bench 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.

Refer to mt_bench/seallm_7b_v2.jsonl for the MT-bench predictions of SeaLLM-7B-v2, and here to reproduce it.

Model Access Langs MT-Bench
GPT-4-turbo closed multi 9.32
GPT-4-0613 closed multi 9.18
Mixtral-8x7b (46B) open multi 8.3
Starling-LM-7B-alpha open mono (en) 8.0
OpenChat-3.5-7B open mono (en) 7.81
SeaLLM-7B-v2 open multi (10+) 7.54
Qwen-14B open multi 6.96
Llama-2-70B open mono (en) 6.86
Mistral-7B-instuct open mono (en) 6.84

Sea-Bench

Similar to MT-Bench, 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.

As shown, the huge improvements come from math-reasoning, reaching GPT-3.5 level of performance.

fig_sea_bench_side_by_side.png

Refer to sea_bench/seallm_7b_v2.jsonl for the Sea-bench predictions of SeaLLM-7B-v2.

Usage

Instruction format 

prompt = """<|im_start|>system
You are a helpful assistant.</s><|im_start|>user
Hello world</s><|im_start|>assistant
Hi there, how can I help?</s>"""

# NOTE: previous commit has \n between </s> and <|im_start|>, that was incorrect!
# <|im_start|> is not a special token.
# Transformers chat_template should be consistent with vLLM format below.

# ! ENSURE 1 and only 1 bos `<s>` at the beginning of sequence
print(tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt)))

'<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁a', '▁helpful', '▁assistant', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '?', '</s>']
"""

Using transformers's chat_template 


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", torch_dtype=torch.bfloat16, device_map=device)
tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM-7B-v2")

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]))
# ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁a', '▁helpful', '▁assistant', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '▁you', '▁today', '?', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Ex', 'plain', '▁general', '▁rel', 'ativity', '▁in', '▁details', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>']

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 

from vllm import LLM, SamplingParams
TURN_TEMPLATE = "<|im_start|>{role}\n{content}</s>"
TURN_PREFIX = "<|im_start|>{role}\n"

# There is no \n between </s> and <|im_start|>.

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=['</s>', '<|im_start|>'])
llm = LLM("SeaLLMs/SeaLLM-7B-v2", 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

Should follow the chat format and accurately mask out source tokens. Here is an example.

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_v2_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}</s>"
    TURN_PREFIX = "<|im_start|>{role}\n"
    sample = None
    assistant_prefix_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))
            turn_sample['token_type_ids'][assistant_prefix_len:] = [1] * (len(turn_sample['input_ids']) - assistant_prefix_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_v2_tokenize_multi_turns(tokenizer, conversations)
print(tokenizer.convert_ids_to_tokens(sample['input_ids']))
print(sample['token_type_ids'])
# ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁hel', 'ful', '▁assistant', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '?', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Tell', '▁me', '▁a', '▁joke', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Why', '▁don', "'", 't', '▁scientists', '▁trust', '▁atoms', '?', '▁Because', '▁they', '▁make', '▁up', '▁everything', '.', '</s>']
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

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

Author list and order will change!

  • * and ^ are equal contributions.
@article{damonlpsg2023seallm,
  author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*,
            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},
}
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