PEFT
Safetensors
blackline-atlas
lfm2.5-vl
lora
satellite-imagery
sentinel-2
simsat
vision-language-model
civilian-disruption-triage
source-led-evidence
humanitarian
Eval Results (legacy)
Instructions to use ChrisRPL/blackline-atlas-lfm25-vl-sft-hf-corpus-full-v1b-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ChrisRPL/blackline-atlas-lfm25-vl-sft-hf-corpus-full-v1b-adapter 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, "ChrisRPL/blackline-atlas-lfm25-vl-sft-hf-corpus-full-v1b-adapter") - Notebooks
- Google Colab
- Kaggle
| { | |
| "adapter_repo_id": "ChrisRPL/blackline-atlas-lfm25-vl-sft-hf-corpus-full-v1b-adapter", | |
| "base_model_id": "LiquidAI/LFM2.5-VL-450M", | |
| "bundle_summary": { | |
| "eval_records": 3421, | |
| "image_files": 16110, | |
| "run_name": "lfm25_vl_sft_hf_corpus_full_v1b", | |
| "source_corpus_repo_id": "ChrisRPL/blackline-atlas-training-corpus-v1", | |
| "source_split_counts": { | |
| "bigearthnet_eval_rows": 1999, | |
| "bigearthnet_train_rows": 17994, | |
| "chatearthnet_eval_rows": 800, | |
| "chatearthnet_train_rows": 7200, | |
| "initial_vlm_eval_rows": 600, | |
| "initial_vlm_train_rows": 5400, | |
| "simsat_gold_eval_rows": 22, | |
| "simsat_gold_train_repeated_rows": 264 | |
| }, | |
| "strategy": "HF Jobs direct materialization; SimSat gold oversampled, context shards normalized to JSON discard targets.", | |
| "train_records": 30858 | |
| }, | |
| "checkpoint_name": "LFM2.5-VL-450M-vlm_sft-train.json-all-lr5em05-w0p1-lora_a-e0s3471-20260502_225652", | |
| "corpus_repo_id": "ChrisRPL/blackline-atlas-training-corpus-v1", | |
| "run_name": "lfm25_vl_sft_hf_corpus_full_v1b" | |
| } | |