Update README.md

README.md

@@ -25,6 +25,11 @@ We introduce [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2), the st
 * 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](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)) of similar sizes.
 
+### Release and DEMO
+
+- DEMO: [SeaLLMs/SeaLLM-7B](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B).
+- Technical report: [Arxiv: SeaLLMs - Large Language Models for Southeast Asia](https://arxiv.org/pdf/2312.00738.pdf).
+- Model weights: [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2).
 
 <blockquote style="color:red">
 <p><strong style="color: red">Terms of Use and License</strong>: 
@@ -36,3 +41,293 @@ By using our released weights, codes, and demos, you agree to and comply with th
 > 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.
 
+
+
+### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1?
+
+* SeaLLM-7B-v2 is continue-pretrained from [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) and underwent carefully designed tuning with focus in reasoning.
+
+
+## Evaluation
+
+
+### Zero-shot CoT Multilingual Math Reasoning
+
+[SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/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](https://huggingface.co/SeaLLMs/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](fig_sea_math_side_by_side.png)
+
+
+<details>
+<summary>See details on English and translated GSM8K and MATH with zero-shot reasoning</summary>
+<br>
+
+| 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
+| 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
+
+</details>
+
+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](https://huggingface.co/SeaLLMs/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](https://huggingface.co/SeaLLMs/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)](https://arxiv.org/pdf/2205.11916.pdf) 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](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json), [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)).
+
+### 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
+| SeaLLM-7B-v2    | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52
+
+
+VMLU reproduce script [here](https://github.com/DAMO-NLP-SG/SeaLLMs/blob/main/evaluation/vmlu/vmlu_run.py). 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](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json)).
+
+
+### MT-Bench
+
+On the English [MT-bench](https://arxiv.org/abs/2306.05685) 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](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/mt_bench/seallm_7b_v2.jsonl) for the MT-bench predictions of SeaLLM-7B-v2, and [here](https://github.com/lm-sys/FastChat/issues/3013#issue-2118685341) 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](https://huggingface.co/Qwen/Qwen-14B-Chat) | open | multi | 6.96
+| [Llama-2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | open | mono (en) | 6.86
+| Mistral-7B-instuct | open | mono (en) | 6.84
+
+
+### Sea-Bench
+
+Similar to MT-Bench, [Sea-bench](https://huggingface.co/datasets/SeaLLMs/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](fig_sea_bench_side_by_side.png)
+
+Refer to [sea_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/sea_bench/seallm_7b_v2.jsonl) for the Sea-bench predictions of SeaLLM-7B-v2.
+
+
+### Usage
+
+#### Instruction format
+
+```python
+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
+```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", 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
+
+```python
+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.
+
+```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_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: [[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*,
+            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},
+}
+```
+