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license: other
library_name: peft
base_model: LiquidAI/LFM2.5-VL-450M
datasets:
- ChrisRPL/blackline-atlas-training-corpus-v1
tags:
- blackline-atlas
- lfm2.5-vl
- peft
- lora
- satellite-imagery
- sentinel-2
- simsat
- vision-language-model
- civilian-disruption-triage
- source-led-evidence
- humanitarian
model-index:
- name: blackline-atlas-lfm25-vl-sft-hf-corpus-full-v1b-adapter
results:
- task:
type: image-to-text
name: Source-led satellite visual site brief
dataset:
type: ChrisRPL/blackline-atlas-training-corpus-v1
name: Blackline Atlas Training Corpus v1
metrics:
- type: eval_loss
value: 0.3273
name: Final eval loss
- type: valid_json
value: 1.0
name: SimSat gold JSON validity
- type: analyst_schema_valid
value: 0.864
name: SimSat gold analyst-schema validity
- type: action_match
value: 0.409
name: SimSat gold action match
---
# Blackline Atlas LFM2.5-VL Adapter
This is the final public Blackline Atlas Liquid-track adapter for the hackathon
submission. It is a PEFT/LoRA adapter for `LiquidAI/LFM2.5-VL-450M`.
## Intended Role
The adapter is a guarded visual site-brief component. It compares a selected
Sentinel/SimSat baseline image and current image, then writes a structured
civilian-scope brief:
- visible scene
- likely visual change
- limitations such as cloud, low resolution, or no-data tiles
- relationship between the source report and what is actually visible
- one of `discard`, `defer`, or `downlink_now`
It is not autonomous alert authority. Blackline Atlas keeps final triage under
deterministic civilian guardrails and fails closed when model output is invalid.
## Training Data
Dataset: [`ChrisRPL/blackline-atlas-training-corpus-v1`](https://huggingface.co/datasets/ChrisRPL/blackline-atlas-training-corpus-v1)
The corpus contains normalized, license-aware training shards for source-led
satellite triage. It is not a raw mirror of third-party datasets. It includes
planner/tool rows, paired-image visual brief rows, hard negatives, and
SimSat/Sentinel examples with provenance/audit notes.
Training split: 30,858 rows.
Eval split: 3,421 rows.
Training job: [`69f66f889d85bec4d76f0be0`](https://huggingface.co/jobs/ChrisRPL/69f66f889d85bec4d76f0be0)
## Objective
Supervised fine-tuning for structured visual brief generation over civilian
infrastructure disruption examples. The model is trained to separate source
facts from satellite-visible facts and to prefer low-confidence discard/defer
when imagery is cloudy, low resolution, or not directly evidentiary.
## Evaluation Summary
- Eval loss improved from `3.0021` to `0.3273`.
- Corpus-native 22-case SimSat gold eval:
- `22 / 22` valid JSON outputs
- `19 / 22` analyst-schema valid reports
- `9 / 22` action matches
These results support a guarded analyst-narration lane. They do not justify
autonomous alerting.
## Runtime Use
Blackline Atlas uses this adapter after a source lead resolves to Sentinel
current/baseline imagery. Contact sheets may be supplied as orientation-only
context. Mapbox tiles are not evidence. SAM/SAM3 masks are not part of the
judge runtime path for low-resolution Sentinel pairs.
Expected JSON actions:
- `discard`
- `defer`
- `downlink_now`
Malformed, tactical, source-only, or low-confidence output should be repaired
only when safe; otherwise it is withheld.
## Safety Boundary
Allowed use: civilian resilience, humanitarian logistics transparency, public
accountability, and macro-scale disruption triage.
Disallowed use: tactical targeting, strike support, military asset ranking,
troop/convoy tracking, sabotage guidance, or claims of real-time surveillance
beyond the source data and satellite imagery.
This adapter is not autonomous alert authority and should not be used as a sole
decision-maker for emergency, military, law-enforcement, or targeting decisions.
## Limitations
- Sentinel imagery may be cloudy, low resolution, stale, or missing.
- The model can overstate source facts if not guarded; runtime code strips or
withholds source-only casualty/impact claims.
- Action match is not strong enough for autonomous scoring.
- Satellite-visible evidence is macro-scale only. Tiny objects and tactical
interpretations are out of scope.
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