Create README.md

README.md

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+---
+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
+pipeline_tag: text-generation
+base_model: SeaLLMs/SeaLLM-7B-v2.5
+---
+
+# SeaLLM-7B-v2.5-GGUF
+- Thsi si quantized version for [SeaLLMs/SeaLLM-7B-v2.5](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5)
+
+
+## Model Description
+
+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).
+
+<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.
+
+![fig_sea_math_side_by_side.png](fig_sea_math_side_by_side.png)
+-->
+
+| 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
+
+![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png)
+
+
+### Usage
+
+#### Instruction format
+
+```python
+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 `<s>` 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},
+}
+```