Qwen3.5-0.8B-mlx-LoRA (Vietnamese)
This model is a LoRA adapter for Qwen3.5-0.8B-4bit, fine-tuned on the Vietnamese Alpaca dataset using the MLX framework on Apple Silicon.
Model Details
Model Description
- Developed by: baeGil
- Model type: LoRA Adapter
- Language(s) (NLP): Vietnamese (vi)
- License: Apache 2.0
- Finetuned from model: mlx-community/Qwen3.5-0.8B-4bit
Model Sources
Uses
Direct Use
This model is intended for chat and assistant tasks in Vietnamese. It can be used to answer general questions, assist in writing, and provide information in Vietnamese.
Out-of-Scope Use
The model should not be used for generating harmful, illegal, or unethical content. It is not suitable for high-stakes medical or legal advice.
Training Details
Training Data
The model was fine-tuned on the 5CD-AI/Vietnamese-alpaca-gpt4-gg-translated dataset, which contains 500 samples of instruction-following data in Vietnamese.
Training Procedure
- Framework: MLX
- Iterations: 50 (experimented with multiple ranks)
- Batch Size: 1
- Learning Rate: 2e-5
- Hardware: Apple M2 Pro (16GB RAM)
Training Hyperparameters
- LoRA Rank: 16 (Optimal)
- LoRA Alpha: 32
- Max Sequence Length: 256
Evaluation
Results
Based on local evaluation on a test set:
- Test Loss: 1.7527
- Perplexity: 5.7702
How to Get Started with the Model
To generate text using this adapter with mlx-vlm:
python -m mlx_vlm.generate \
--model-path mlx-community/Qwen3.5-0.8B-4bit \
--adapter-path baeGil/Qwen3.5-0.8B-mlx-LoRA \
--prompt "### Instruction:\nCách nấu phở bò truyền thống?\n\n### Response:\n" \
--max-tokens 256
Environmental Impact
- Hardware Type: Apple M2 Pro
- Time used: ~12 minutes
- Carbon Emitted: Negligible due to high efficiency of Apple Silicon.
Model Card Contact
[baeGil]
Github Repository
Hardware compatibility
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