---
license: other
library_name: transformers
tags:
- llama-factory
- full
- generated_from_trainer
base_model: hon9kon9ize/CantoneseLLM-v1.0-72B-cpt
model-index:
- name: CantoneseLLMChat-v1.0-72B
  results: []
---


# CantoneseLLMChat-v1.0-72B

![front_image](cantonese_llm_v1.jpg)


Cantonese LLM Chat v1.0 is the first generation Cantonese LLM from hon9kon9ize.
Building upon the sucess of [v0.5 preview](https://huggingface.co/hon9kon9ize/CantoneseLLMChat-v0.5), the model excels in Hong Kong related specific knowledge and Cantonese conversation.

## Model description
Base model obtained via Continuous Pre-Training of [Qwen 2.5 72B](https://huggingface.co/Qwen/Qwen2.5-72B) with 600 millions publicaly available Hong Kong news articles and Cantonese websites. 
Instructions fine-tuned model trained with a dataset consists of 75,000 instrutions pairs. 45,000 pairs were Cantonese insturctions generated by other LLMs and reviewed by humans. 

The model trained with 16 Nvidia H100 96GB HBM2e GPUs on [Genkai Supercomputer](https://www.cc.kyushu-u.ac.jp/scp/eng/system/Genkai/hardware/).

## Basic Usage
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-72B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16, 
    device_map="auto", 
)
def chat(messages, temperature=0.9, max_new_tokens=200):
    input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to('cuda:0')
    output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature)
    response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=False)
    return response
prompt = "邊個係香港特首？"
messages = [
    {"role": "system", "content": "you are a helpful assistant."},
    {"role": "user", "content": prompt}
]
print(chat(messages)) # 香港特別行政區行政長官係李家超。<|im_end|>
```


## Performance
Best in class open source LLM in understanding Cantonese and Hong Kong culture in the [HK-Eval Benchmark](https://arxiv.org/pdf/2503.12440). 
However, as one could observe, reasoning models have performed dramatically better than their counterparts. We are currently working on reasoning models for v2.

| Model                     | HK Culture (zero-shot) | Cantonese Linguistics |
|---------------------------|:----------------------:|:---------------------:|
| CantonesellmChat v0.5 6B  |          52.0%         |         12.8%         |
| CantonesellmChat v0.5 34B |          72.5%         |         54.5%         |
| CantonesellmChat v1.0 3B  |          56.0%         |         45.7%         |
| CantonesellmChat v1.0 7B  |          60.3%         |         46.5%         |
| CantonesellmChat v1.0 32B |          69.8%         |         52.7%         |
| CantonesellmChat v1.0 72B |          75.4%         |         59.6%         |
| Llama 3.1 8B Instruct     |          45.6%         |         35.1%         |
| Llama 3.1 70B Instruct    |          63.0%         |         50.3%         |
| Qwen2.5 7B Instruct       |          51.2%         |         30.3%         |
| Qwen2.5 32B Instruct      |          59.9%         |         45.1%         |
| Qwen2.5 72B Instruct      |          65.9%         |         45.9%         |
| Claude 3.5 Sonnet         |          71.7%         |         63.2%         |
| DeepSeek R1               |          88.8%         |         77.5%         |
| Gemini 2.0 Flash          |          80.2%         |         75.3%         |
| Gemini 2.5 Pro            |          92.1%         |         87.3%         |
| GPT4o                     |          77.5%         |         63.8%         |
| GPT4o-mini                |          55.6%         |         57.3%         |