Instructions to use LiquidAI/d1-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use LiquidAI/d1-2B-GGUF 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 LiquidAI/d1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/d1-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LiquidAI/d1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/d1-2B-GGUF:Q4_K_M

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 LiquidAI/d1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LiquidAI/d1-2B-GGUF:Q4_K_M

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 LiquidAI/d1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiquidAI/d1-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LiquidAI/d1-2B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use LiquidAI/d1-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/d1-2B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/d1-2B-GGUF",
		"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/LiquidAI/d1-2B-GGUF:Q4_K_M

Ollama
How to use LiquidAI/d1-2B-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/d1-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use LiquidAI/d1-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LiquidAI/d1-2B-GGUF:Q4_K_M

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": "LiquidAI/d1-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LiquidAI/d1-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/d1-2B-GGUF:Q4_K_M
```

Lemonade

How to use LiquidAI/d1-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/d1-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.d1-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LiquidAI/d1-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LiquidAI/d1-2B-GGUF:Q4_K_M

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 LiquidAI/d1-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/d1-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LiquidAI/d1-2B-GGUF:Q4_K_M

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 "LiquidAI/d1-2B-GGUF:Q4_K_M" \
  --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"

API Try Docs Blog

d1-2B-GGUF

GGUF version of d1-2B for local inference on CPUs and GPUs.

d1-2B is a decision model for text, pictures, and video, built on LFM3-VL-2B. It returns calibrated answers in one forward pass, without generating tokens.

Find architecture and usage details in the original model card.

Use with llama.cpp

Supported in llama.cpp b11960 (v0.8.0) and newer.

llama.cpp answers noul, choice and score questions over a state (text or JSON), pictures or a video. Video input needs ffmpeg in PATH. Multiple choice ("multiple": true), extraction, estimates and boxes are not supported yet.

llama-server -hf LiquidAI/d1-2B-GGUF:Q8_0 -b 8192 -ub 8192

A question is read in one batch, so -b and -ub must hold its state and media. A picture takes at most half of -ub: 8192 keeps the full budget of 4096 tokens per picture, smaller values lower it.

Then send questions to the /v1/systemone endpoint.

Text

curl http://127.0.0.1:8080/v1/systemone -H "Content-Type: application/json" -d '{
  "state": "I was charged twice this month, please refund one of them.",
  "questions": {
    "refund":  {"type": "noul", "instructions": "Is the customer asking for a refund?"},
    "team":    {"type": "choice", "instructions": "Which team should handle this?",
                "criteria": {"billing": "Charges, refunds, invoices", "technical": "App or site faults",
                             "fraud": "Suspected unauthorised use"}},
    "urgency": {"type": "score", "instructions": "How urgent is this?",
                "criteria": ["Can wait", "Today", "Blocking the customer now"]}
  }
}'

Image + Text

curl -sL -o cats.jpg http://images.cocodataset.org/val2017/000000039769.jpg  # two cats on a sofa

curl http://127.0.0.1:8080/v1/systemone -H "Content-Type: application/json" -d @- <<JSON
{
  "state": "Photo attached to a pet-sitting request.",
  "images": ["data:image/jpeg;base64,$(base64 < cats.jpg | tr -d '\n')"],
  "questions": {
    "pet":  {"type": "choice", "instructions": "Which animals are in the photo?",
             "criteria": {"cats": "Cats", "dogs": "Dogs", "birds": "Birds"}},
    "sofa": {"type": "noul", "instructions": "Are the animals on a sofa?"}
  }
}
JSON

Video + Text

curl -sL -o reading.mp4 https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/4cba700bd771f44d72b549253da025c32e944d42/sample_demo_1.mp4  # a child reading a book

curl http://127.0.0.1:8080/v1/systemone -H "Content-Type: application/json" -d @- <<JSON
{
  "state": null,
  "files": ["data:video/mp4;base64,$(base64 < reading.mp4 | tr -d '\n')"],
  "questions": {
    "activity": {"type": "choice", "instructions": "What is the child doing?",
                 "criteria": {"reading": "Reading a book", "eating": "Eating", "sleeping": "Sleeping"}},
    "glasses":  {"type": "noul", "instructions": "Is the child wearing glasses?"}
  }
}
JSON

The state can be null when the pictures or the video are the whole state.

Downloads last month: 1,101

GGUF

Model size

2B params

Architecture

lfm3

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for LiquidAI/d1-2B-GGUF

Base model

LiquidAI/LFM3-1.7B-Base

Finetuned

LiquidAI/LFM3-VL-2B

Finetuned

LiquidAI/d1-2B

Quantized

(6)

this model