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
| { | |
| "generated_by": "ml/training/modal_lora_train.py", | |
| "record_version": "phase6.checkpoint.v1", | |
| "result": { | |
| "adapter_artifact_ref": "modal-volume://landfillsentry-model-artifacts/lora_run_20260504T181913Z/checkpoint-lora-v1", | |
| "artifact_volume": "landfillsentry-model-artifacts", | |
| "checkpoint_dir": "data/processed/hf_adapter_trained/lora_run_20260504T181913Z/checkpoint-lora-v1", | |
| "local_adapter_dir": "data/processed/hf_adapter_trained/lora_run_20260504T181913Z/checkpoint-lora-v1", | |
| "metrics": { | |
| "adapter_status": "fine_tuned", | |
| "base_model": "LiquidAI/LFM2.5-VL-450M", | |
| "base_revision": "main", | |
| "completed_steps": 24, | |
| "created_at": "2026-05-04T18:19:13.913342+00:00", | |
| "dataset_manifest_checksum": "a6738e1af7d89f6fbd0d567c89759f6103beaa81553074a3ad520c6810988b01", | |
| "dataset_sample_count": 78, | |
| "eval_loss_after": 1.3696070164442062, | |
| "eval_loss_before": 2.410613179206848, | |
| "eval_loss_delta": 1.041006162762642, | |
| "lora_alpha": 16, | |
| "lora_dropout": 0.05, | |
| "lora_r": 8, | |
| "max_steps": 24, | |
| "target_modules": [ | |
| "k_proj", | |
| "q_proj", | |
| "v_proj" | |
| ], | |
| "train_loss_mean": 1.8549085905154545, | |
| "train_sample_count_used": 49, | |
| "validation_sample_count_used": 8 | |
| }, | |
| "model_id": "LiquidAI/LFM2.5-VL-450M", | |
| "revision": "main", | |
| "run_id": "lora_run_20260504T181913Z", | |
| "status": "ok", | |
| "training_mode": "peft_lora_supervised" | |
| } | |
| } | |