metadata

license: other
library_name: transformers
tags:
  - llama-factory
  - full
  - generated_from_trainer
base_model: hon9kon9ize/CantoneseLLM-v1.0-32B-cpt
model-index:
  - name: CantoneseLLMChat-v1.0-32B
    results: []

CantoneseLLMChat-v1.0-32B

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 32B 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 4 Nvidia H100 80GB HBM3 GPU on Genkai Supercomputer.

Basic Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-32B"
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|>