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VinUni-lab21-Finetuning-w-LoRA-QLoRA-best-rank – AI Model by DangMinh21 | AlphaNeural AI

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DangMinh21/
VinUni-lab21-Finetuning-w-LoRA-QLoRA-best-rank

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unsloth/Llama-3.2-3B-Instruct-bnb-4bit

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DangMinh21/VinUni-lab21-Finetuning-w-LoRA-QLoRA-best-rank

LoRA/QLoRA adapter trained for Lab 21 rank experiment.

Training Setup

Base model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
Dataset: 5CD-AI/Vietnamese-alpaca-gpt4-gg-translated
Sample size after cleaning: 273
Train/eval split: 245/28 with seed 42
Max sequence length: 1024
Epochs: 3
Learning rate: 0.0002
Scheduler: cosine
Batch size: 1
Gradient accumulation: 8
Effective batch size: 8
Optimizer: adamw_8bit
Packing: False

Adapter

Run ID: qv-r32
Target modules: qv
Rank: 32.0
Alpha: 64.0
Trainable parameters: 9175040.0
Trainable percent: 0.506170363612226

Evaluation

Eval loss: 1.7458990322692054
Eval perplexity: 5.731051560317372
Peak VRAM GB: 7.862848512
Train time minutes: 4.827954991658529

See the GitHub/Colab report for qualitative comparisons and rank trade-off analysis.