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

Use Docker

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

Llama new
LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/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": "AtomicChat/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/AtomicChat/d1-2B-GGUF:Q4_K_M

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

How to use AtomicChat/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 AtomicChat/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": "AtomicChat/d1-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/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 AtomicChat/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 "AtomicChat/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"

d1-2B — GGUF (by AtomicChat)

Built by the team behind Atomic Chat, the app that runs open-weight models locally and offline.

Atomic-Dynamic (AD) GGUF quants of LiquidAI/d1-2B, Liquid's decision model (classification, extraction, scoring, calibration) on the LFM3-VL backbone. It answers a typed question — yes/no, a choice, or a score — with a probability over the options, served by llama.cpp's /v1/systemone.

Rungs

same-answer is how often the rung returns the same decision as the original BF16, over a 30-language set; mean TVD is the average total-variation distance between the two option-probability distributions (lower is better). The decision head is robust to quantization, so fidelity stays high across the ladder.

Rung	GB on disk	bpw	same-answer	mean TVD
Q8_0	1.85	8.51	99.3%	0.004
AD-Q6_K	1.55	7.31	98.7%	0.007
AD-Q5_K_M	1.29	6.03	97.5%	0.014
AD-Q4_K_M	1.12	5.20	96.3%	0.022
AD-IQ4_XS	1.05	5.02	95.9%	0.023
AD-IQ3_S	0.95	4.23	94.4%	0.041

AD-Q4_K_M is the recommended default. The vision projector mmproj-d1-2B-F16.gguf is included — d1-2B takes images and text.

Run (llama.cpp)

Needs a build with the d1 / LFM3 decision path (merged in llama.cpp #31275; these files were produced at llama.cpp d05706b4). Serve it and query /v1/systemone:

llama-server -hf AtomicChat/d1-2B-GGUF:AD-Q4_K_M --mmproj mmproj-d1-2B-F16.gguf
# POST /v1/systemone  {"state": "<your text/image context>", "questions": {"q": {"type": "choice", ...}}}