342 lines
16 KiB
Markdown
342 lines
16 KiB
Markdown
---
|
||
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
|
||
---
|
||
|
||
<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>
|
||
|
||
# <strong style="color: red">BIG NEWS: <a href="https://huggingface.co/SeaLLMs/SeaLLM3-7B-Chat">SeaLLM3</a> 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.</strong>
|
||
|
||
🔥<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: [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},
|
||
}
|
||
```
|
||
|