README.md

---
language:
- zh
- en
license: apache-2.0
tags:
- Cantonese
- Qwen2
- chat
- mlx
datasets:
- jed351/cantonese-wikipedia
- raptorkwok/cantonese-traditional-chinese-parallel-corpus
pipeline_tag: text-generation
model-index:
- name: Qwen2-Cantonese-7B-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: 54.35
      name: strict accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Qwen2-Cantonese-7B-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: 32.45
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Qwen2-Cantonese-7B-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: 8.76
      name: exact match
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Qwen2-Cantonese-7B-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: 6.04
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Qwen2-Cantonese-7B-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: 7.81
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Qwen2-Cantonese-7B-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: 31.59
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lordjia/Qwen2-Cantonese-7B-Instruct
      name: Open LLM Leaderboard
library_name: mlx
---

> This is a MLX conversion of [lordjia/Qwen2-Cantonese-7B-Instruct](https://huggingface.co/lordjia/Qwen2-Cantonese-7B-Instruct)

# Qwen2-Cantonese-7B-Instruct

## Model Overview / 模型概述

Qwen2-Cantonese-7B-Instruct is a Cantonese language model based on Qwen2-7B-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.

Qwen2-Cantonese-7B-Instruct係基於Qwen2-7B-Instruct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**: Qwen2-7B-Instruct
- **Fine-tuning Method**: LoRA instruction tuning
- **Training Steps**: 4572 steps
- **Primary Language**: Cantonese / 粵語
- **Datasets**:
  - [jed351/cantonese-wikipedia](https://huggingface.co/datasets/jed351/cantonese-wikipedia)
  - [raptorkwok/cantonese-traditional-chinese-parallel-corpus](https://huggingface.co/datasets/raptorkwok/cantonese-traditional-chinese-parallel-corpus)
- **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: [qwen2-cantonese-7b-instruct-q4_0.gguf](https://huggingface.co/lordjia/Qwen2-Cantonese-7B-Instruct/blob/main/qwen2-cantonese-7b-instruct-q4_0.gguf).

此外，仲提供此模型嘅4位量化版本：[qwen2-cantonese-7b-instruct-q4_0.gguf](https://huggingface.co/lordjia/Qwen2-Cantonese-7B-Instruct/blob/main/qwen2-cantonese-7b-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. [Llama-3-Cantonese-8B-Instruct](https://huggingface.co/lordjia/Llama-3-Cantonese-8B-Instruct) based on Meta-Llama-3-8B-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 Apache 2.0 license. Please review the terms before use.

此模型喺Apache 2.0許可證下獲得許可。 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__Qwen2-Cantonese-7B-Instruct)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |23.50|
|IFEval (0-Shot)    |54.35|
|BBH (3-Shot)       |32.45|
|MATH Lvl 5 (4-Shot)| 8.76|
|GPQA (0-shot)      | 6.04|
|MuSR (0-shot)      | 7.81|
|MMLU-PRO (5-shot)  |31.59|