Instructions to use AtomicChat/d1-3B-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/d1-3B-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/d1-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/d1-3B-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/d1-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/d1-3B-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/d1-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/d1-3B-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/d1-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/d1-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AtomicChat/d1-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use AtomicChat/d1-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/d1-3B-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/d1-3B-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/d1-3B-GGUF:Q4_K_M

Ollama
How to use AtomicChat/d1-3B-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/d1-3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use AtomicChat/d1-3B-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/d1-3B-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/d1-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AtomicChat/d1-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/d1-3B-GGUF:Q4_K_M
```

Lemonade

How to use AtomicChat/d1-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/d1-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.d1-3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AtomicChat/d1-3B-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/d1-3B-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/d1-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/d1-3B-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/d1-3B-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/d1-3B-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"

How to Run d1-3B Locally

Built from Liquid AI's published weights with our own importance matrix, and measured on decisions, not just text.

d1-3B is Liquid AI's decision model. It takes a state and answers typed questions in one forward pass: yes or no, a pick from named options, or a score. The answer is read from the model's distribution over the options' tokens, so nothing is generated.

Pick a file

Every number below is measured on one machine. The raw results and logs are in the metrics repo.

same answer: across 1,285 decisions, how often the file gives the same answer as the BF16 weights published in LiquidAI/d1-3B. The questions are yes/no, choice and score questions over held-out texts in 30 languages and source code. The number of changed answers is in brackets.
option drift: the mean total variation distance between the file's option probabilities and the original's. 0 means identical.
KLD and top-1: full-vocabulary agreement with the original on held-out text.

File	Size	same answer	option drift	KLD	top-1
`BF16`	5,403 MB on disk	reference	0	0	100%
`Q8_0`	2,875 MB on disk	99.3% (9)	0.0033	0.0010	98.28%
`AD-Q6_K`	2,348 MB on disk	99.1% (11)	0.0053	0.0022	97.41%
`AD-Q5_K_M`	1,953 MB on disk	98.1% (25)	0.0112	0.0080	95.09%
`AD-Q4_K_M`	1,658 MB on disk	97.1% (37)	0.0178	0.0244	91.54%
`AD-IQ4_XS`	1,570 MB on disk	95.6% (57)	0.0203	0.0283	90.92%

AD- means Atomic Dynamic: the type is chosen per tensor instead of taken from a llama.cpp preset.

The token table, which d1-3B also uses as its output head, never goes below Q6_K.
Attention stays at Q8_0 or Q6_K.
ffn_down in the first and last four blocks takes one step more than the middle.

The projector for images comes as mmproj-d1-3B-BF16 and mmproj-d1-3B-Q8_0. We did not measure image decisions.

How these compare to other GGUFs

Liquid's own GGUFs are built from the current weights and carry the type lfm2-d1. Their BF16 has the same tensors as ours.
prithivMLmods/d1-3B-GGUF has llama.cpp's stock presets, also from the current weights.

We measured all of them on one machine (an NVIDIA A10, llama.cpp 88dcc46) against our BF16. This run is separate from the table above, so our own rows differ from it slightly.

File	Size	same answer	option drift	option KL	KLD	top-1
AtomicChat `Q8_0`	2,875 MB	99.2% (10)	0.0034	0.0001	0.0009	98.30%
Liquid `Q8_0`	2,875 MB	99.4% (8)	0.0034	0.0001	0.0009	98.30%
AtomicChat `AD-Q6_K`	2,348 MB	99.3% (9)	0.0054	0.0002	0.0022	97.41%
prithivMLmods `Q6_K`	2,222 MB	98.7% (17)	0.0077	0.0004	0.0041	96.34%
AtomicChat `AD-Q5_K_M`	1,953 MB	97.7% (29)	0.0112	0.0009	0.0081	95.11%
prithivMLmods `Q5_K_M`	1,940 MB	97.7% (29)	0.0138	0.0014	0.0121	93.89%
AtomicChat `AD-Q4_K_M`	1,658 MB	97.0% (38)	0.0174	0.0023	0.0243	91.55%
Liquid `Q4_K_M`	1,674 MB	95.7% (55)	0.0270	0.0053	0.0392	89.42%
prithivMLmods `Q4_K_M`	1,674 MB	95.9% (53)	0.0272	0.0053	0.0389	89.44%

4 bits. Our AD-Q4_K_M is 16 MB smaller than the two stock Q4_K_M files.
- It changes 38 answers instead of 55.
- Its option KL is less than half of theirs, and its KLD is 38% lower.
5 bits. At about the same size as prithivMLmods' Q5_K_M, ours changes the same number of answers, with about a third less option KL and KLD.
6 bits. Our AD-Q6_K is 126 MB larger than prithivMLmods' Q6_K, so the two are not a same-size comparison.
Q8_0. The two Q8_0 files measure the same.
- A few tensors round differently, because Liquid's converter writes Q8_0 itself.
- 8 against 10 changed answers is within run-to-run noise. Our own Q8_0 changed 9 in the run above.

Liquid's first GGUFs, published on 6 October, were built from the previous version of the weights. Liquid replaced them on 7 October. Our measurement of those first files is in the metrics repo.

Running it

This needs a llama.cpp build that includes #30110: commit 88dcc46, merged on 7 October, or newer. Older builds do not know the type lfm2-d1 and refuse to load the file.

llama-server -m d1-3B-AD-Q4_K_M.gguf --mmproj mmproj-d1-3B-Q8_0.gguf -ngl 99 -c 8192

Ask through the /v1/systemone endpoint. It takes a state and named, typed questions, and returns each answer with its option probabilities:

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": {
    "team": {"type": "choice", "instructions": "Which team should handle this?",
             "criteria": {"billing": "Charges, refunds, invoices", "technical": "App or site faults",
                          "fraud": "Suspected unauthorised use"}},
    "angry": {"type": "noul", "instructions": "Is the customer angry?"}
  }
}'

Response from AD-Q4_K_M, numbers rounded:

{
  "answers": {
    "team": {"type": "choice", "choice": "billing",
             "probabilities": {"billing": 0.984, "technical": 0.011, "fraud": 0.005}, "confidence": 0.977},
    "angry": {"type": "noul", "noul": 0.099}
  },
  "usage": {"input_tokens": 97, "output_tokens": 0}
}

noul questions are yes or no, and score questions take a list of 2 to 10 levels.
Images go in an images array as data URLs.
The full request format is in the server documentation, and the question schema in the original card.

Checked on the merged endpoint. We ran the PR's final commit against Liquid's own PyTorch code in FP32, on the same 1,285 text decisions:

Q8_0 changes 6 answers, with an option KL of 0.0001.
AD-Q4_K_M changes 44, with an option KL of 0.0022.
These numbers use a different reference and a different readout from the table above, so they do not match it exactly.

How we made and measured them

BF16: converted from LiquidAI/d1-3B (revision da1fe36) with llama.cpp 18b5f8b. It matches Liquid's PyTorch code on 300 decisions with a mean option KL of 0.0002. The 9 changed answers there are near-ties, with a largest option difference of 0.035. The projector is tensor-for-tensor identical to Liquid's.
Importance matrix: 1.5M tokens of decision prompts, 3,206 in all. The states come from the calibration corpora pool (Wikipedia in 30 languages, code, structured files). Each carries yes/no, choice and score questions and is rendered with the model's own prompt code.
Decisions: 1,285 questions over the held-out calib-corpora eval/neutral and eval/code texts, none of them in the calibration set. Before the endpoint existed, they were answered from the option tokens' log-probabilities on /completion: the prompt is rendered with Liquid's prompt.py, and the readout is a softmax over the options' tokens. The scripts are in the metrics repo.
Text: llama-perplexity --kl-divergence over eval/neutral, 93 chunks of 4,096 tokens.
Setup: an NVIDIA H100 and llama.cpp 18b5f8b for every file.
Metadata: on 7 October, after #30110 was merged, the files were re-stamped. The text files got lfm2.decision.type = lfm2-d1 and the systemone template, both taken from a BF16 file the PR's converter wrote. The projectors got clip.vision.image_resize_algo = bicubic. No tensor changed.

Model details

Base: LiquidAI/d1-3B, a decision model post-trained from LFM2.5-VL-3B: 3.1B parameters, 32K context, a 128K vocabulary and a SigLIP2 vision encoder.
License: LFM Open License v1.0; see LICENSE.