|
--- |
|
language: |
|
- en |
|
- zh |
|
- vi |
|
- id |
|
- th |
|
- ms |
|
- km |
|
- lo |
|
- my |
|
- tl |
|
license: other |
|
tags: |
|
- multilingual |
|
- sea |
|
license_name: seallms |
|
license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE |
|
--- |
|
|
|
<p align="center"> |
|
<img src="seal_logo.png" width="200" /> |
|
</p> |
|
|
|
# *SeaLLM-7B-v2.5* - Large Language Models for Southeast Asia |
|
|
|
|
|
<p align="center"> |
|
<a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Website</a> |
|
|
|
<a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5" target="_blank" rel="noopener"> 🤗 Tech Memo</a> |
|
|
|
<a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B-v2.5" target="_blank" rel="noopener"> 🤗 DEMO</a> |
|
|
|
<a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a> |
|
|
|
<a href="https://arxiv.org/pdf/2312.00738.pdf" target="_blank" rel="noopener">Technical Report</a> |
|
</p> |
|
|
|
🔥<span style="color: #ff3860">[HOT]</span> 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 (`<eos>` 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) |
|
|
|
<blockquote style="color:red"> |
|
<p><strong style="color: red">Terms of Use and License</strong>: |
|
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>. |
|
</blockquote> |
|
|
|
> **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<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>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 |
|
|
|
<!-- |
|
[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. |
|
|
|
 |
|
--> |
|
|
|
| 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 |
|
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
|
| 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 |
|
|
|
 |
|
|
|
|
|
### Usage |
|
|
|
**IMPORTANT NOTICE for using the model** |
|
|
|
* `<bos>` must be at start of prompt, ff your code's tokenizer does not prepend `<bos>` by default, you MUST prepend <bos> 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 <bos> by default, |
|
# You MUST prepend <bos> into the prompt yourself, otherwise, it would not work! |
|
|
|
prompt = """<|im_start|>system |
|
You are a helpful assistant.<eos> |
|
<|im_start|>user |
|
Hello world<eos> |
|
<|im_start|>assistant |
|
Hi there, how can I help?<eos>""" |
|
|
|
# <|im_start|> is not a special token. |
|
# Transformers chat_template should be consistent with vLLM format below. |
|
|
|
# ! ENSURE 1 and only 1 bos `<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}<eos>\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=['<eos>', '<|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}<eos>\n" |
|
TURN_PREFIX = "<|im_start|>{role}\n" |
|
TURN_SUFFIX = "<eos>\n" |
|
TURN_SUFFIX_TAKE = "<eos>" |
|
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 <eos> is trained (token_type 1) |
|
# [('<bos>', 0), ('<', 0), ('|', 0), ..., ('assistant', 0), ('\n', 0), ('Hi', 1), ('▁there', 1), (',', 1), ('▁how', 1), ('▁can', 1), ('▁I', 1), ('▁help', 1), ('?', 1), ('<eos>', 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: [[email protected]](mailto:[email protected]) |
|
|
|
**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}, |
|
} |
|
``` |
|
|
|
|
