CantoneseLLMChat-v1.0-7B

Cantonese LLM Chat v1.0 is the first generation Cantonese LLM from hon9kon9ize. Building upon the sucess of v0.5 preview, 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 7B 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 1 Nvidia H100 80GB HBM3 GPU on Genkai Supercomputer.

Basic Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-7B"


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. 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%