Instructions to use jpmarindiaz/lfm2-flood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpmarindiaz/lfm2-flood with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="jpmarindiaz/lfm2-flood")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("jpmarindiaz/lfm2-flood")
model = AutoModelForImageTextToText.from_pretrained("jpmarindiaz/lfm2-flood")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use jpmarindiaz/lfm2-flood with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jpmarindiaz/lfm2-flood",
	filename="lfm2-flood-Q4_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jpmarindiaz/lfm2-flood with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jpmarindiaz/lfm2-flood:Q4_0
# Run inference directly in the terminal:
llama-cli -hf jpmarindiaz/lfm2-flood:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jpmarindiaz/lfm2-flood:Q4_0
# Run inference directly in the terminal:
llama-cli -hf jpmarindiaz/lfm2-flood:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jpmarindiaz/lfm2-flood:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf jpmarindiaz/lfm2-flood:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jpmarindiaz/lfm2-flood:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jpmarindiaz/lfm2-flood:Q4_0

Use Docker

docker model run hf.co/jpmarindiaz/lfm2-flood:Q4_0

LM Studio
Jan

vLLM

How to use jpmarindiaz/lfm2-flood with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpmarindiaz/lfm2-flood"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpmarindiaz/lfm2-flood",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/jpmarindiaz/lfm2-flood:Q4_0

SGLang

How to use jpmarindiaz/lfm2-flood with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jpmarindiaz/lfm2-flood" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpmarindiaz/lfm2-flood",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jpmarindiaz/lfm2-flood" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpmarindiaz/lfm2-flood",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use jpmarindiaz/lfm2-flood with Ollama:
```
ollama run hf.co/jpmarindiaz/lfm2-flood:Q4_0
```

Unsloth Studio new

How to use jpmarindiaz/lfm2-flood with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jpmarindiaz/lfm2-flood to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jpmarindiaz/lfm2-flood to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jpmarindiaz/lfm2-flood to start chatting

Pi new

How to use jpmarindiaz/lfm2-flood with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jpmarindiaz/lfm2-flood:Q4_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jpmarindiaz/lfm2-flood:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jpmarindiaz/lfm2-flood with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jpmarindiaz/lfm2-flood:Q4_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jpmarindiaz/lfm2-flood:Q4_0

Run Hermes

hermes

Docker Model Runner
How to use jpmarindiaz/lfm2-flood with Docker Model Runner:
```
docker model run hf.co/jpmarindiaz/lfm2-flood:Q4_0
```

Lemonade

How to use jpmarindiaz/lfm2-flood with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jpmarindiaz/lfm2-flood:Q4_0

Run and chat with the model

lemonade run user.lfm2-flood-Q4_0

List all available models

lemonade list

lfm2-flood

Fine-tune of LiquidAI/LFM2.5-VL-450M for flood detection from Sentinel-2 satellite tile pairs (RGB + SWIR baseline + RGB + SWIR current). Output is a structured JSON flood-risk profile.

The model is intended to run on a low-resource node (a satellite or a community ground station) and downlink only the JSON payload — not raw imagery — for offline humanitarian response in flood-affected regions.

Status: experimental. Trained on 88 paired samples (110 total, 22 held out for eval). The dataset is too small and noisy for an operational alert system; this is the infrastructure, not a deployable model. See the Limitations section.

Inputs

Four images per inference, in this order:

RGB-baseline (B4-B3-B2 true color) — the location at a pre-event timestamp
SWIR-baseline (B12-B8-B4 false color) — same timestamp
RGB-current — the location at the timestamp to assess
SWIR-current — same timestamp

Each is a Sentinel-2 PNG, ~5 km × 5 km, served as base64 data URL or HF Hub URL.

Output

{
  "flood_present": true,
  "flood_severity": "moderate",
  "water_coverage_pct_estimate": "30-60%",
  "populated_area_affected": true,
  "infrastructure_at_risk": true,
  "river_overflow_visible": true,
  "image_quality_limited": false
}

7 fields. flood_present and the four booleans are change-relative — i.e. "is there flooding vs the baseline tile", not "is there water in this tile". image_quality_limited is the abstention signal — set true on cloudy or partial-coverage tiles.

Files

file	size	purpose
`model.safetensors`	~860 MB	merged HF checkpoint, full fine-tune of LFM2.5-VL-450M
`config.json`, `tokenizer*.json`, `chat_template.jinja`, etc.	small	standard HF transformers metadata
`lfm2-flood-Q4_0.gguf`	~245 MB	quantized backbone for llama.cpp
`mmproj-lfm2-flood-F16.gguf`	~189 MB	vision tower + projector for llama.cpp

Quick start (llama.cpp)

# Download both files (or use `hf download`)
hf download jpmarindiaz/lfm2-flood lfm2-flood-Q4_0.gguf --local-dir .
hf download jpmarindiaz/lfm2-flood mmproj-lfm2-flood-F16.gguf --local-dir .

# Serve OpenAI-compatible endpoint
llama-server -m lfm2-flood-Q4_0.gguf --mmproj mmproj-lfm2-flood-F16.gguf -c 8192 --port 8765

Then POST 4 images to /v1/chat/completions with a JSON-schema response_format. Full client code in the humaid repo (finetune-flood/src/evaluate.ts, finetune-flood/app/server.ts).

Training

Base model: LiquidAI/LFM2.5-VL-450M
Backend: leap-finetune on Modal H100
Training type: vlm_sft, full fine-tune (no LoRA — the multimodal projector needs to relearn satellite multispectral imagery)
Hyperparameters: 3 epochs, per-device batch 2, gradient accumulation 8 (effective 16), LR 5e-5 cosine, vision-encoder LR multiplier 0.5, bf16
Wall time: ~70 seconds on H100
Dataset: 88 train + 22 eval pair samples from 9 documented La Mojana / Putumayo flood events. See jpmarindiaz/flood-detection-pair-colombia.

Evaluation

Compared against the labeler's self-consistency oracle and the un-fine-tuned base model on the same 110 pair samples:

field	opus oracle (n=30)	base LFM2.5-VL (n=110)	this model (n=110)
valid_json	1.00	1.00	1.00
fields_present	1.00	1.00	1.00
flood_present	0.67	0.66	0.66
flood_severity	0.43	0.29	0.29
water_coverage_pct_estimate	0.70	0.37	0.35
populated_area_affected	0.73	0.51	0.51
infrastructure_at_risk	0.73	0.54	0.54
river_overflow_visible	0.67	0.60	0.60
image_quality_limited	0.83	0.10	0.90
overall	0.68	0.44	0.55
avg latency (s)	3.87	0.53	0.53

overall is the macro-average across the 7 fields. valid_json and fields_present are 1.0 because we use grammar-constrained JSON output via response_format: {type: "json_schema"}.

Limitations

Sentinel-2 wet-season cloud cover. ~50% of acquisitions over La Mojana in Apr–Jun and Aug–Nov are >50% cloud. Operational pipelines (CopernicusLAC) use Sentinel-1 SAR (cloud-independent) for the same task. This model was trained only on Sentinel-2.
Inter-labeler noise floor caps the ceiling. Opus self-consistency on the hardest schema fields is 0.43–0.70. The student model can't exceed inter-labeler agreement.
The fine-tune learned the dataset prior, not the task. The +11-point overall gain (0.44 → 0.55) is concentrated almost entirely in image_quality_limited (0.10 → 0.90). Other fields essentially didn't move with 88 train samples × 3 epochs.
Geographic scope: Colombia only. Locations are 8 La Mojana municipalities + 6 Putumayo. May not generalize to other flood regimes (e.g. flash floods, glacial-melt rivers, hurricane storm surge).

How it was built

The full pipeline + scripts + playbook are in the humaid repo on GitHub. Key files:

finetune-flood/PLAYBOOK.md — end-to-end command sequence (fetch → label → build → upload → train → pull → package → serve → eval)
finetune-flood/REPORT.md — wrap-up findings (what worked, what didn't, the case for switching to Sentinel-1 SAR before resuming)
finetune-flood/docs/ — overview, pipeline, data collection, labeling, evaluation, findings
finetune-flood/scripts/convert_mmproj_lfm2vl.py — patch around the upstream convert_hf_to_gguf.py issue with lm_head.weight in full-FT merged checkpoints
finetune-flood/app/ — small Hono app for testing the model interactively

Citation

Trained 2026-05-08 as part of the humaid project — offline-first humanitarian response toolkit for flood crises in Colombia. Built on:

LFM2.5-VL-450M by Liquid AI
leap-finetune by Liquid AI
SimSat by DPhi Space
The Liquid AI × DPhi Space AI in Space hackathon wildfire-prevention example by Pau Labarta Bajo

Downloads last month: 227

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for jpmarindiaz/lfm2-flood

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

LiquidAI/LFM2.5-VL-450M

Quantized

(21)

this model

jpmarindiaz
/

lfm2-flood