Instructions to use VoltageVagabond/spam-classifier-liquid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use VoltageVagabond/spam-classifier-liquid with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct") model = PeftModel.from_pretrained(base_model, "VoltageVagabond/spam-classifier-liquid") - Notebooks
- Google Colab
- Kaggle
File size: 3,149 Bytes
5a28a50 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | #!/bin/bash
# =============================================================
# Spam Classifier — Liquid AI LoRA Retrain
# Double-click to choose fast or full retrain mode.
# Replaces retrain-fast.command and retrain-full.command.
# =============================================================
cd "$(dirname "$0")"
source venv/bin/activate
echo "============================================================"
echo " Liquid AI LoRA Retrain — Spam / Ham / Phishing"
echo " Model: LiquidAI/LFM2.5-1.2B-Instruct"
echo "============================================================"
echo ""
echo " f) Fast retrain — ~8,000 examples, ~1-1.5 hours"
echo " u) Full retrain — ~16,000 examples, ~2 hours"
echo " q) Quit"
echo ""
echo " Memory optimizations: MPS cache flush, gradient checkpointing, bf16, fused AdamW."
echo ""
read -p "Choice [f/u/q]: " MODE_CHOICE
case "$MODE_CHOICE" in
q|Q)
echo "Bye!"
sleep 2
exit 0
;;
f|F)
MODE="fast"
ADAPTER_DIR="$(dirname "$0")/adapters_fast"
echo ""
echo " Starting FAST retrain (~8K examples, ~1-1.5 hours)..."
;;
u|U)
MODE="full"
ADAPTER_DIR="$(dirname "$0")/adapters_full"
echo ""
echo " Starting FULL retrain (~16K examples, ~2 hours)..."
;;
*)
echo "Invalid choice."
sleep 3
exit 1
;;
esac
echo ""
python3 "../new_training_data/retrain_liquid.py" --mode "$MODE"
RETRAIN_STATUS=$?
if [[ $RETRAIN_STATUS -ne 0 ]]; then
echo ""
echo "Retrain failed (exit $RETRAIN_STATUS)."
echo ""
read -p "Press any key to close..."
exit 1
fi
echo ""
echo "============================================================"
echo " Retrain complete!"
echo " New adapter: $ADAPTER_DIR"
echo "============================================================"
echo ""
echo "Would you like to make this the default adapter?"
echo " - Backs up current adapters/ -> adapters_backup/"
echo " - Copies adapters_${MODE}/ -> adapters/"
echo " - The app and notebook will use the new adapter automatically"
echo ""
read -p "Swap in as default? [y/N]: " SWAP
if [[ "$SWAP" == "y" || "$SWAP" == "Y" ]]; then
PROJ_DIR="$(dirname "$0")"
if [[ -d "$PROJ_DIR/adapters" ]] && [[ ! -d "$PROJ_DIR/adapters_backup" ]]; then
mv "$PROJ_DIR/adapters" "$PROJ_DIR/adapters_backup"
echo " Backed up adapters/ -> adapters_backup/"
elif [[ -d "$PROJ_DIR/adapters" ]]; then
rm -rf "$PROJ_DIR/adapters_old_backup"
mv "$PROJ_DIR/adapters_backup" "$PROJ_DIR/adapters_old_backup" 2>/dev/null
mv "$PROJ_DIR/adapters" "$PROJ_DIR/adapters_backup"
echo " Backed up adapters/ -> adapters_backup/"
fi
cp -r "$ADAPTER_DIR" "$PROJ_DIR/adapters"
echo " Copied adapters_${MODE}/ -> adapters/"
echo " The app and notebook now use the new adapter!"
else
echo " Skipped. To use later, copy adapters_${MODE}/ to adapters/"
fi
echo ""
echo "Next: run Retrain.command in the LLM Project folder to"
echo "rebuild the GGUF and upload to HuggingFace."
echo ""
read -p "Press any key to close..."
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