How to Use Feast for SLM/LLM Post-Training (with Ray)
| Name |
Type |
Fields |
| web_documents |
FeatureView |
human, bot, human_repeat_ratio, bot_repeat_ratio |
| train_example |
OnDemandFeatureView |
cleaned_human, cleaned_bot, char_count, is_trainable, sft_text |
| llm_posttrain |
FeatureService |
web_documents + train_example |
Source data is prepared parquet (document_id + event_timestamp already present). No Feast core patches.
| Flag |
What happens |
| (default) |
to_ray_dataset() + preprocess sft_text (ODFV does not run) |
| --via-df |
to_df() so ODFV train_example runs |
uv pip install -e "../../sdk/python[ray]" -r requirements.txt
PYTHONPATH=../../sdk/python python scripts/prepare_data.py
cd feature_repo && feast apply && cd ..
PYTHONPATH=../../sdk/python python scripts/train_sft.py --dry-run
PYTHONPATH=../../sdk/python python scripts/train_sft.py --dry-run --via-df
How to Use Feast for SLM/LLM Post-Training with Ray