Instructions to use XXXpilar/Fastfill-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use XXXpilar/Fastfill-test with PEFT:
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- Notebooks
- Google Colab
- Kaggle
Download code/fastfill/v2/ops/run_structured.sh from XXXpilar/Fastfill-test: direct link, hf CLI and curl.
- Browser
- Download file 928 Bytes
-
https://huggingface.co/XXXpilar/Fastfill-test/resolve/main/code/fastfill/v2/ops/run_structured.sh
- Command line
-
hf download hf://XXXpilar/Fastfill-test/code/fastfill/v2/ops/run_structured.sh
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curl -L -o run_structured.sh https://huggingface.co/XXXpilar/Fastfill-test/resolve/main/code/fastfill/v2/ops/run_structured.sh
928 Bytes
| # Two OptiScene-style LLM agent modes on the frozen 300 rows, then the same scorer as every other method. | |
| set -uo pipefail | |
| cd /home/jovyan/shanliantian/fastfill | |
| export PYTHONDONTWRITEBYTECODE=1 TOKENIZERS_PARALLELISM=false | |
| ROWS=runs/llm-prompt-300/rows.jsonl; ENV=/home/jovyan/shanliantian/.fastfill_api.env | |
| for mode in structured structured-harness; do | |
| ( ./env/bin/python -m fastfill.v2.llm_structured --data $ROWS --env $ENV --mode $mode --repairs 2 --workers 4 --max-samples 300 \ | |
| --output runs/llm-$mode-300 > runs/llm-$mode-300.log 2>&1 | |
| cmp -s runs/llm-$mode-300/rows.jsonl $ROWS && echo "$mode rows identical" || echo "$mode ROWS DIFFER" | |
| ./env/bin/python -m fastfill.v2.evaluate --data runs/llm-$mode-300/rows.jsonl --predictions runs/llm-$mode-300/predictions.jsonl \ | |
| --output runs/llm-$mode-300-eval > runs/llm-$mode-300-eval.log 2>&1 && echo "$mode scored" ) & | |
| done | |
| wait | |
| echo ALL-DONE | |