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:
- Training Tools: 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.
此模型的4位量化版本也可用: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:
- Qwen2-Cantonese-7B-Instruct based on Qwen2-7B-Instruct.
- 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
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
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 |
- Downloads last month
- 681
Model tree for lordjia/Llama-3-Cantonese-8B-Instruct
Datasets used to train lordjia/Llama-3-Cantonese-8B-Instruct
Spaces using lordjia/Llama-3-Cantonese-8B-Instruct 6
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard66.690
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard26.790
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard8.230
- acc_norm on GPQA (0-shot)Open LLM Leaderboard5.820
- acc_norm on MuSR (0-shot)Open LLM Leaderboard9.480
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard27.940