---
license: other
license_name: lfm-1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-VL-450M/blob/main/LICENSE
base_model: LiquidAI/LFM2.5-VL-450M
library_name: transformers
pipeline_tag: image-text-to-text
language:
  - en
tags:
  - vision-language
  - earth-observation
  - remote-sensing
  - sentinel-2
  - vrsbench
  - tailings-dam
  - gistm
  - compliance
  - lora
  - lfm2-vl
  - liquid-ai
  - satdiff
---

# SatDiff-LFM2.5-VL-450M-stage1

A LoRA fine-tune of `LiquidAI/LFM2.5-VL-450M` trained to emit per-claim, contract-framed evidence on Sentinel-2 imagery of regulated dams and tailings storage facilities. Built for the **SatDiff** submission to the Liquid AI "AI in Space" hackathon (Liquid Track, 2026).

This is the *evidence-text writer* component of the SatDiff pipeline. It does not perform threshold-based severity classification on its own — that work is done deterministically by a Python rules engine downstream of the model. See **Stage 2 (negative result)** below for the methodological reasoning.

## What it does

Given (a) Sentinel-2 imagery (RGB + NIR composites for baseline + current pass) and (b) a contract memo prompt that lists per-claim evidence-sourcing rules, this model produces a structured per-claim evidence string for a 5-claim audit schema covering:

1. Impoundment morphology (deposition asymmetry, footprint change)
2. Pond management (pond-to-wall distance, area change, turbidity, NDWI)
3. Retaining-wall integrity (gully count + width, NDMI on the wall face, SWIR anomaly)
4. Deformation (declares "no SAR data available" when SAR is absent)
5. Protected-zone encroachment (towns, residential extensions)

## Headline result (held-out evaluation)

The base `LFM2.5-VL-450M` parrots the same three indices into every claim's evidence regardless of which physical signal the claim is about. Stage 1 fine-tuning — without any SatDiff-specific examples — teaches the model to read the per-claim sourcing rules from the prompt and cite the right diff fields.

| metric (held-out backtest passes, 6 dates × 5 claims = 30 claims) | base | Stage 1 |
|---|---:|---:|
| schema-valid passes | 6/6 | 6/6 |
| **evidence-correct claims** | **0/30 (0 %)** | **30/30 (100 %)** |

The lift comes from generic VRSBench grounding, not from domain-specific examples.

## Training

- **Framework**: [`Liquid4All/leap-finetune`](https://github.com/Liquid4All/leap-finetune) (Ray Train + Accelerate, managed via `uv`). Not raw `transformers + peft`.
- **Base model**: `LiquidAI/LFM2.5-VL-450M`.
- **Dataset**: VRSBench (NeurIPS 2024) — 5 000 captioning + VQA samples (no `[refer]` grounding tasks).
- **Method**: LoRA SFT, rank as configured in the recipe, 2 epochs.
- **Hardware**: single RTX 4080 Laptop, 12 GB VRAM, WSL2 + CUDA 12.6.
- **Wall-clock**: 38 m 15 s.
- **Eval loss**: base ~3.21 → Stage 1 **1.41** (−56 %).

## Stage 2 (negative result, not shipped)

A second-stage fine-tune was attempted using 29 hand-authored examples (boundary, escalation, routine, catastrophic regimes) plus 17 auto-generated examples from the SatDiff Phase 2 backtest, split 35 train / 11 held-out before training. Stage 2 preserved Stage 1's 100 % evidence-correctness but **worsened** severity adherence on real held-out data (5 → 9 corrections by the downstream rules engine).

Diagnosis: the hand-authored cases used contrived metric values around the threshold cliffs (e.g. pond-to-wall = 24 m vs 26 m); the held-out real-data passes lived in a different distribution (pond-to-wall ≈ 9.9 m throughout the failure window). LoRA at this scale (35 examples × 3 epochs ≈ 31 effective steps) cannot reshape multi-tier threshold reasoning.

We ship Stage 1, not Stage 2. The Stage 2 checkpoint is intentionally not uploaded — it would confuse the model-card story.

This is the strongest possible validation of the SatDiff *rules-engine architecture*: severity, action, escalation, and overall-status are computed deterministically by `phase2.aggregate.compute_severity` from physical-diff numbers, *regardless* of what the model emits. Stage 2 attempting and failing to lift the model's threshold reasoning confirms that this work belongs in deterministic Python at our scale.

## Files in this repo

- **fp16 transformers checkpoint** (`model.safetensors` + `config.json` + `tokenizer.json` + `chat_template.jinja` + …) — load via `transformers.AutoModelForImageTextToText`.
- **GGUF pair** (`gguf/LFM2.5-VL-450M-stage1-Q8_0.gguf` + `gguf/mmproj-LFM2.5-VL-450M-stage1-Q8_0.gguf`) — load via `llama.cpp` / `llama-server`. 362 MB Q8_0 backbone + 182 MB F16 mmproj = 544 MB total.

## Reproducing the headline result

```bash
# 1. Pull the SatDiff repo + Stage 1 GGUF, bring up the stack:
git clone <satdiff-repo>
cd satdiff
docker compose up -d

# 2. Run the Phase 2 backtest against the Stage 1 model:
python -m phase2.cli --asset jagersfontein \
  --date-range 2021-06-15,2022-10-15 \
  --inference llama_server \
  --model WobblyDopamine/SatDiff-LFM2.5-VL-450M-stage1-Q8_0

# 3. Render the per-pass PDF audit reports:
python -m phase3 --asset jagersfontein --date-range 2021-06-15,2022-10-15
```

End-to-end latency on RTX 4080 Laptop (sm_89, CUDA 12.6) using the GGUF + llama-server path: **2.38 s/pass** — 4.7× over the bf16 transformers path with no schema or evidence-quality regression.

## Intended use

This model is the *evidence-text writer* for the SatDiff TSF/dam compliance pipeline. It is **not** a general-purpose VLM and is **not** a severity classifier on its own. Use it as part of the rules-engine pipeline described above.

## Limitations

- Trained on a small VRSBench slice; performance outside the SatDiff contract prompt's sourcing rules is unknown.
- Held-out evaluation was on 6 Jagersfontein passes; transfer to other TSF/dam assets is plausible (the lift comes from generic EO grounding) but unmeasured.
- Severity grading is performed by a downstream Python rules engine, not by this model. Do not delegate threshold reasoning to the fine-tuned weights at this scale.
- Cloud-cover gating is upstream; the model is not robust to severe cloud occlusion.

## License

This work is released under the **LFM Open License v1.0**, inherited from the base model `LiquidAI/LFM2.5-VL-450M`. See the upstream license at https://huggingface.co/LiquidAI/LFM2.5-VL-450M/blob/main/LICENSE.

## Citation

```bibtex
@misc{satdiff2026,
  title={SatDiff: A Satellite-Readable Compliance Contract for Tailings and Dam Monitoring},
  author={Scholz, Peter},
  year={2026},
  howpublished={Liquid AI "AI in Space" Hackathon submission, Liquid Track},
  note={Fine-tune of LiquidAI/LFM2.5-VL-450M on VRSBench. Stage 1 evidence-correctness 0/30 → 30/30 on held-out tailings-dam passes. See https://huggingface.co/WobblyDopamine/SatDiff-LFM2.5-VL-450M-stage1.}
}
```

## Acknowledgements

- Liquid AI for the LFM2.5-VL-450M base model and the `leap-finetune` framework.
- DPhi for the SimSat API and the hackathon platform.
- Torres-Cruz & O'Donovan (2023) for the *Scientific Reports* reconstruction of the Jagersfontein failure that anchored this submission's validation.