lucylq/qwen3_06B_lora_math

Model Card for Model ID

Qwen0.6B trained on the MetaMathQA dataset using Unsloth. Used to test ExecuTorch LoRA capabilities.

Training Data

Dataset: https://huggingface.co/datasets/meta-math/MetaMathQA

Training Configuration

OUTPUT_DIR = "./outputs"
BATCH_SIZE = 2  # Smaller batch for longer sequences
GRADIENT_ACCUMULATION_STEPS = 8  # Effective batch = 16
LEARNING_RATE = 2e-4
NUM_EPOCHS = 1  # MetaMathQA is large, 1 epoch is often enough
WARMUP_RATIO = 0.03
LOGGING_STEPS = 25
SAVE_STEPS = 500
MAX_SAMPLES = 50000  # Limit samples for faster training (set None for full dataset)

Training Hyperparameters

Using bf16, which is what the original Qwen0.6B checkpoint it.

Framework versions

• PEFT 0.18.0

ExecuTorch Files

These are Qwen3 0.6B models, lowered to XNNPACK, quantized with torchao 8da4w and embedding quantization following the export script in: https://github.com/meta-pytorch/executorch-examples/blob/main/program-data-separation/export_lora.sh

See the corresponding README in: https://github.com/meta-pytorch/executorch-examples/tree/main/program-data-separation/cpp/lora_example

• qwen3_06B_q.ptd: foundation weights
• qwen3_06B_q.pte: base model
• qwen3_06B_lora_q.ptd: lora weights
• qwen3_06B_lora_q.pte: lora model

To run the model, please download the Qwen tokenizer from: https://huggingface.co/Qwen/Qwen-tokenizer/tree/main