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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("jpmarindiaz/lfm2-flood")
model = AutoModelForMultimodalLM.from_pretrained("jpmarindiaz/lfm2-flood", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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 Desktop

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 serve -hf jpmarindiaz/lfm2-flood:Q4_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

How to use jpmarindiaz/lfm2-flood with OpenClaw:

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "jpmarindiaz/lfm2-flood:Q4_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

lfm2-flood

File size: 2,216 Bytes

570f5f1

{
  "best_global_step": null,
  "best_metric": null,
  "best_model_checkpoint": null,
  "epoch": 2.571428571428571,
  "eval_steps": 50,
  "global_step": 15,
  "is_hyper_param_search": false,
  "is_local_process_zero": true,
  "is_world_process_zero": true,
  "log_history": [
    {
      "epoch": 0,
      "eval_loss": 2.4168105125427246,
      "eval_runtime": 0.8445,
      "eval_samples_per_second": 4.737,
      "eval_steps_per_second": 4.737,
      "step": 0
    },
    {
      "epoch": 0.19047619047619047,
      "grad_norm": 0.3839778006076813,
      "learning_rate": 0.0,
      "loss": 2.4852142333984375,
      "lr/language_model": 5e-05,
      "lr/multi_modal_projector": 5e-05,
      "lr/vision_tower": 2.5e-05,
      "step": 1
    },
    {
      "epoch": 0.9523809523809523,
      "grad_norm": 0.13315580785274506,
      "learning_rate": 2.2272893530850373e-05,
      "loss": 1.0789419412612915,
      "lr/language_model": 4.058724504646834e-05,
      "lr/multi_modal_projector": 4.058724504646834e-05,
      "lr/vision_tower": 2.029362252323417e-05,
      "step": 5
    },
    {
      "epoch": 1.7619047619047619,
      "grad_norm": 0.02488594874739647,
      "learning_rate": 9.718488325546072e-06,
      "loss": 0.12491919994354247,
      "lr/language_model": 1.4152906522061048e-05,
      "lr/multi_modal_projector": 1.4152906522061048e-05,
      "lr/vision_tower": 7.076453261030524e-06,
      "step": 10
    },
    {
      "epoch": 2.571428571428571,
      "grad_norm": 0.01759636588394642,
      "learning_rate": 3.134010977272048e-07,
      "loss": 0.05445554852485657,
      "lr/language_model": 0.0,
      "lr/multi_modal_projector": 0.0,
      "lr/vision_tower": 0.0,
      "step": 15
    }
  ],
  "logging_steps": 5,
  "max_steps": 15,
  "num_input_tokens_seen": 0,
  "num_train_epochs": 3,
  "save_steps": 500,
  "stateful_callbacks": {
    "TrainerControl": {
      "args": {
        "should_epoch_stop": false,
        "should_evaluate": false,
        "should_log": false,
        "should_save": true,
        "should_training_stop": true
      },
      "attributes": {}
    }
  },
  "total_flos": 581461637857280.0,
  "train_batch_size": 2,
  "trial_name": null,
  "trial_params": null
}