ChrisRPL/lfm25-vl-civilian-conflict-reporter-lora-v1

LFM2.5-VL Civilian Conflict Disruption Reporter LoRA v1

Diagnostic PEFT LoRA adapter for LiquidAI/LFM2.5-VL-450M, trained on ChrisRPL/satellite-civilian-conflict-disruption-reporter-v1.

Status

This is a diagnostic local LoRA artifact, not the accepted Blackline Atlas adapter and not the demo-critical runtime model.

Important audit note: the attempted Hugging Face Jobs run 69f22721d2c8bd8662bd3151 failed before training because the job command referenced a local absolute path that is not visible inside the remote HF Jobs container. Therefore this repo should not be described as a completed HF Jobs training result.

The current canonical Blackline adapter artifact remains ChrisRPL/blackline-atlas-lfm25-vl-sft-train-hf-aux-v10-adapter, and that adapter is also published only as a rejected research artifact after failing the eval-gold schema/action smoke gate.

Intended task

Paired baseline/current satellite-image reporting for macro-visible civilian disruption from conflict, bombardment, explosion, shelling, or related human-caused disruption. The output is evidence-first JSON, not tactical guidance.

The adapter must not be used for military asset detection, route intelligence, targeting, strike support, or ranking of targets.

Expected output schema

{
  "visible_change_summary": "string",
  "civilian_disruption_evidence": ["collapsed_building", "debris_field"],
  "negative_evidence": ["no_visible_change"],
  "uncertainty_factors": ["string"],
  "severity_hint": "none | low | medium | high",
  "recommended_action": "discard | defer | downlink_now",
  "confidence": 0.0,
  "short_rationale": "string"
}

Training summary

• Method: TRL SFTTrainer VLM SFT with PEFT LoRA.
• Base model: LiquidAI/LFM2.5-VL-450M.
• Dataset: ChrisRPL/satellite-civilian-conflict-disruption-reporter-v1.
• Diagnostic subset: 32 train rows, 16 eval rows.
• Epochs: 1.
• Batch size: 1, gradient accumulation: 8.
• Learning rate: 5e-5, cosine scheduler, warmup ratio 0.05.
• LoRA: r=8, alpha=16, dropout=0.05, target_modules=all-linear.
• Local run final train loss: 8.816; eval loss: 7.937.
• Trackio dashboard: https://huggingface.co/spaces/ChrisRPL/mlintern-lfm25v1

This is a diagnostic adapter, not a production model.

Promotion status

Not promoted. Before any future promotion, rerun this training path from a self-contained HF Jobs script or bundle, then require eval-gold generation to produce schema-valid JSON, improved action match, nonzero downlink_now recall, and no false-positive regression.

Known limitations

• Very small diagnostic run; loss remains high.
• Labels are partly inherited/rule-derived, not all expert-reviewed.
• BRIGHT rows are optical-to-SAR and introduce cross-modality artifacts.
• Local generation evaluation showed the base model did not satisfy the strict schema; adapter evaluation is limited and should be repeated on GPU before demo promotion.
• Associated HF Job 69f22721d2c8bd8662bd3151 failed before training and is not evidence of successful remote fine-tuning.