014ec8bd5c
- Training data pipeline: convert, export, extract, load-to-db - Infra tooling: infra-audit, infra-gitea-link - RAG pipeline: rag-ingest, rag-query - Fine-tuning: finetune-lora, overnight-qwen3, install-unsloth - Transcripts: export-transcripts - Updated README with script index and token reduction strategy
57 lines
1.6 KiB
Bash
Executable File
57 lines
1.6 KiB
Bash
Executable File
#!/bin/bash
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# Install unsloth for Qwen3-8B LoRA fine-tuning on GTX 1080 (Pascal, CUDA 12.x)
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# Uses GPU 1 (GTX 1080, 8GB VRAM) — leaves GPU 0 free for inference
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# Run once. Safe to re-run.
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set -e
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echo "=== Unsloth install for Grace fine-tuning (GPU 1, GTX 1080) ==="
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echo "CUDA: $(nvcc --version 2>/dev/null | grep release || echo 'checking docker...')"
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echo ""
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# Use a dedicated venv to avoid polluting system Python
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VENV_DIR="$HOME/unsloth-env"
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if [ ! -d "$VENV_DIR" ]; then
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echo "[1/5] Creating venv at $VENV_DIR..."
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python3 -m venv "$VENV_DIR"
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else
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echo "[1/5] Venv already exists at $VENV_DIR"
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fi
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source "$VENV_DIR/bin/activate"
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echo "[2/5] Installing PyTorch with CUDA 12.1 (Pascal-compatible)..."
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pip install --quiet torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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echo "[3/5] Installing unsloth (Qwen3 + LoRA support)..."
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pip install --quiet "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
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echo "[4/5] Installing training utilities..."
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pip install --quiet \
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transformers \
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datasets \
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trl \
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peft \
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accelerate \
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bitsandbytes \
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wandb \
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scipy
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echo "[5/5] Verifying install..."
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python3 -c "
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import torch
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print(f'PyTorch: {torch.__version__}')
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print(f'CUDA available: {torch.cuda.is_available()}')
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if torch.cuda.is_available():
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print(f'GPU 0: {torch.cuda.get_device_name(0)}')
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print(f'GPU 1: {torch.cuda.get_device_name(1)}')
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import unsloth
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print(f'Unsloth: OK')
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"
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echo ""
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echo "=== Install complete ==="
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echo "Activate with: source $VENV_DIR/bin/activate"
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echo "Run fine-tuning with: ~/scripts/finetune-lora.py"
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