File size: 2,042 Bytes
92c0ea5 | 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 | # Setup Guide
## Step 1: Create a Virtual Environment
```bash
cd /path/to/spam-classifier-liquid
python3 -m venv venv
source venv/bin/activate
```
You should see `(venv)` at the start of your terminal prompt.
## Step 2: Install Dependencies
```bash
pip install -r requirements.txt
```
This installs PyTorch, HuggingFace Transformers, TRL, PEFT, Gradio, and other packages. It may take a few minutes.
## Step 3: Copy Training Data
The training data comes from the MLX sibling project. Copy it:
```bash
mkdir -p training_data
cp ../spam-classifier-mlx/training_data/train.jsonl training_data/
cp ../spam-classifier-mlx/training_data/test.jsonl training_data/
```
Verify the files:
```bash
wc -l training_data/train.jsonl training_data/test.jsonl
```
You should see ~3,200 lines in train.jsonl and ~800 in test.jsonl.
## Step 4: Fine-Tune the Model
```bash
python3 fine_tune.py
```
This will:
1. Download the Liquid AI model from HuggingFace (~2.4 GB, first run only)
2. Train LoRA adapters on the spam/ham data (3 epochs, ~2-2.5 hours on Apple Silicon)
3. Save the trained adapter to `adapters/`
**Tip:** The notebook (`spam_classifier_liquid.ipynb`) runs only 1 epoch (~45 minutes) for a quicker demo. Use `fine_tune.py` for the full 3-epoch training when you have time.
## Step 5: Launch the Web App
```bash
python3 app.py
```
Open http://127.0.0.1:7860 in your browser.
## Alternative: Double-Click Launchers
On macOS, you can double-click these files in Finder instead of using the terminal:
- `retrain.command` — runs fine_tune.py
- `launch UI.command` — starts the web app
## Verifying Your Setup
Run this quick check to make sure everything is installed correctly:
```python
python3 -c "
import torch
import transformers
import peft
import trl
print(f'PyTorch: {torch.__version__}')
print(f'MPS available: {torch.backends.mps.is_available()}')
print(f'Transformers: {transformers.__version__}')
print(f'PEFT: {peft.__version__}')
print(f'TRL: {trl.__version__}')
print('All good!')
"
```
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