---
language:
- en
- zh
license: llama3
tags:
- Cantonese
- chat
- Llama3
datasets:
- jed351/cantonese-wikipedia
- lordjia/Cantonese_English_Translation
pipeline_tag: text-generation
model-index:
- name: Llama-3-Cantonese-8B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 66.69
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Llama-3-Cantonese-8B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 26.79
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Llama-3-Cantonese-8B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 8.23
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Llama-3-Cantonese-8B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 5.82
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Llama-3-Cantonese-8B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 9.48
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Llama-3-Cantonese-8B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 27.94
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Llama-3-Cantonese-8B-Instruct
      name: Open LLM Leaderboard
---

# Llama-3-Cantonese-8B-Instruct

## Model Overview / 模型概述

Llama-3-Cantonese-8B-Instruct is a Cantonese language model based on Meta-Llama-3-8B-Instruct, fine-tuned using LoRA. It aims to enhance Cantonese text generation and comprehension capabilities, supporting various tasks such as dialogue generation, text summarization, and question-answering.

Llama-3-Cantonese-8B-Instruct係基於Meta-Llama-3-8B-Struct嘅粵語語言模型，使用LoRA進行微調。 它旨在提高粵語文本的生成和理解能力，支持各種任務，如對話生成、文本摘要和問答。

## Model Features / 模型特性

- **Base Model**: Meta-Llama-3-8B-Instruct
- **Fine-tuning Method**: LoRA instruction tuning
- **Training Steps**: 4562 steps
- **Primary Language**: Cantonese / 粵語
- **Datasets**:
  - [jed351/cantonese-wikipedia](https://huggingface.co/datasets/jed351/cantonese-wikipedia)
  - [lordjia/Cantonese_English_Translation](https://huggingface.co/datasets/lordjia/Cantonese_English_Translation)
- **Training Tools**: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)

## Quantized Version / 量化版本

A 4-bit quantized version of this model is also available: [llama3-cantonese-8b-instruct-q4_0.gguf](https://huggingface.co/lordjia/Llama-3-Cantonese-8B-Instruct/blob/main/llama3-cantonese-8b-instruct-q4_0.gguf).

此模型的4位量化版本也可用：[llama3-cantonese-8b-instruct-q4_0.gguf](https://huggingface.co/lordjia/Llama-3-Cantonese-8B-Instruct/blob/main/llama3-cantonese-8b-instruct-q4_0.gguf)。

## Alternative Model Recommendations / 備選模型舉薦

For alternatives, consider the following models, both fine-tuned by LordJia on Cantonese language tasks:

揾其他嘅話，可以諗下呢啲模型，全部都係LordJia用廣東話嘅工作調教好嘅：

1. [Qwen2-Cantonese-7B-Instruct](https://huggingface.co/lordjia/Qwen2-Cantonese-7B-Instruct) based on Qwen2-7B-Instruct.
2. [Llama-3.1-Cantonese-8B-Instruct](https://huggingface.co/lordjia/Llama-3.1-Cantonese-8B-Instruct) based on Meta-Llama-3.1-8B-Instruct.

## License / 許可證

This model is licensed under the Llama 3 Community License. Please review the terms before use.

此模型根據Llama 3社區許可證獲得許可。 請在使用前仔細閱讀呢啲條款。

## Contributors / 貢獻

- LordJia [https://ai.chao.cool](https://ai.chao.cool/)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lordjia__Llama-3-Cantonese-8B-Instruct)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |24.16|
|IFEval (0-Shot)    |66.69|
|BBH (3-Shot)       |26.79|
|MATH Lvl 5 (4-Shot)| 8.23|
|GPQA (0-shot)      | 5.82|
|MuSR (0-shot)      | 9.48|
|MMLU-PRO (5-shot)  |27.94|