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Instructions to use CODELOCKHF/CODELOCK-8Bbeta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CODELOCKHF/CODELOCK-8Bbeta with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "CODELOCKHF/CODELOCK-8Bbeta") - Notebooks
- Google Colab
- Kaggle
File size: 805 Bytes
031e289 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | #!/usr/bin/env bash
# Linux/RunPod: isolated Python environment reusing the image's CUDA torch.
set -euo pipefail
cd "$(dirname "$0")/.."
python3 -m venv --system-site-packages .venv-v2
PYTHON=.venv-v2/bin/python
"$PYTHON" -m pip install -e '.[dev]'
nvidia-smi --query-gpu=name,memory.total --format=csv
"$PYTHON" - <<'PY'
from pathlib import Path
from huggingface_hub import snapshot_download
target = Path('models/Qwen3-8B')
index = target / 'model.safetensors.index.json'
if not index.is_file():
snapshot_download('Qwen/Qwen3-8B', local_dir=target,
allow_patterns=['*.json', '*.safetensors', '*.txt', '*.model', 'tokenizer*'])
print('Base model:', target.resolve())
PY
"$PYTHON" -m pytest
echo 'Ready. Run: .venv-v2/bin/python scripts/run_pipeline.py --model models/Qwen3-8B'
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