Instructions to use akashreddy2103/landfill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use akashreddy2103/landfill with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-VL-450M") model = PeftModel.from_pretrained(base_model, "akashreddy2103/landfill") - Notebooks
- Google Colab
- Kaggle
| """Run Phase 7 evaluation harness and reliability checks.""" | |
| from __future__ import annotations | |
| import json | |
| import sys | |
| from pathlib import Path | |
| PROJECT_ROOT = Path(__file__).resolve().parents[1] | |
| if str(PROJECT_ROOT) not in sys.path: | |
| sys.path.insert(0, str(PROJECT_ROOT)) | |
| from ml.evaluation.phase7_harness import Phase7EvaluationHarness | |
| from ml.evaluation.reliability_harness import ReliabilityHarness | |
| def _load_env_file(path: Path) -> None: | |
| if not path.exists(): | |
| return | |
| import os | |
| for raw_line in path.read_text(encoding="utf-8").splitlines(): | |
| line = raw_line.strip() | |
| if not line or line.startswith("#") or "=" not in line: | |
| continue | |
| key, value = line.split("=", 1) | |
| os.environ.setdefault(key.strip(), value.strip().strip('"').strip("'")) | |
| def _write_json(path: Path, payload: dict) -> None: | |
| path.parent.mkdir(parents=True, exist_ok=True) | |
| path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") | |
| def main() -> None: | |
| _load_env_file(PROJECT_ROOT / ".env.local") | |
| output_dir = PROJECT_ROOT / "data" / "manifests" | |
| evaluation_report_path = output_dir / "phase7_evaluation_report.json" | |
| null_scene_report_path = output_dir / "phase7_null_scene_report.json" | |
| reliability_report_path = output_dir / "phase7_reliability_report.json" | |
| rubric_path = output_dir / "phase7_human_actionability_rubric_v1.json" | |
| comparison_table_path = output_dir / "phase7_baseline_comparison.md" | |
| evaluator = Phase7EvaluationHarness(project_root=PROJECT_ROOT) | |
| evaluation_report = evaluator.run() | |
| reliability_report = ReliabilityHarness().run_all() | |
| null_scene_report = { | |
| "report_version": "phase7.null_scene.v2", | |
| "generated_at": evaluation_report["generated_at"], | |
| "models": evaluation_report["null_scene_report"], | |
| "confidence_intervals": { | |
| model_key: diagnostics["confidence_intervals"]["null_false_positive_rate"] | |
| for model_key, diagnostics in evaluation_report["model_diagnostics"].items() | |
| }, | |
| } | |
| rubric_doc = { | |
| "rubric_version": "phase7.human_actionability.v1", | |
| "generated_at": evaluation_report["generated_at"], | |
| "criteria": evaluation_report["human_rubric"]["criteria"], | |
| "model_rows": evaluation_report["human_rubric"]["model_rows"], | |
| } | |
| _write_json(evaluation_report_path, evaluation_report) | |
| _write_json(null_scene_report_path, null_scene_report) | |
| _write_json(reliability_report_path, reliability_report) | |
| _write_json(rubric_path, rubric_doc) | |
| comparison_table_path.write_text(evaluation_report["comparison_table_markdown"], encoding="utf-8") | |
| print("Phase 7 evaluation complete:") | |
| print(f"- Evaluation report: {evaluation_report_path}") | |
| print(f"- Baseline comparison table: {comparison_table_path}") | |
| print(f"- Null-scene report: {null_scene_report_path}") | |
| print(f"- Human rubric: {rubric_path}") | |
| print(f"- Reliability report: {reliability_report_path}") | |
| if __name__ == "__main__": | |
| main() | |