Instructions to use AtomicChat/Inkling-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AtomicChat/Inkling-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AtomicChat/Inkling-GGUF",
	filename="IQ1_M-final/Inkling-Atomic-IQ1_M-00001-of-00006.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AtomicChat/Inkling-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/Inkling-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Inkling-GGUF:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/Inkling-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Inkling-GGUF:IQ1_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/Inkling-GGUF:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/Inkling-GGUF:IQ1_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/Inkling-GGUF:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/Inkling-GGUF:IQ1_M

Use Docker

docker model run hf.co/AtomicChat/Inkling-GGUF:IQ1_M

LM Studio
Jan

vLLM

How to use AtomicChat/Inkling-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/Inkling-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/Inkling-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/Inkling-GGUF:IQ1_M

Ollama
How to use AtomicChat/Inkling-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/Inkling-GGUF:IQ1_M
```

Unsloth Studio

How to use AtomicChat/Inkling-GGUF 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 AtomicChat/Inkling-GGUF 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 AtomicChat/Inkling-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AtomicChat/Inkling-GGUF to start chatting

How to use AtomicChat/Inkling-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/Inkling-GGUF:IQ1_M

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": "AtomicChat/Inkling-GGUF:IQ1_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AtomicChat/Inkling-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/Inkling-GGUF:IQ1_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/Inkling-GGUF:IQ1_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AtomicChat/Inkling-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/Inkling-GGUF:IQ1_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/Inkling-GGUF:IQ1_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"

Docker Model Runner
How to use AtomicChat/Inkling-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/Inkling-GGUF:IQ1_M
```

Lemonade

How to use AtomicChat/Inkling-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/Inkling-GGUF:IQ1_M

Run and chat with the model

lemonade run user.Inkling-GGUF-IQ1_M

List all available models

lemonade list

Inkling-GGUF / README.md

worthant

forge: regenerate the model card

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	---
	license: apache-2.0
	license_link: https://www.apache.org/licenses/LICENSE-2.0
	thumbnail: https://huggingface.co/AtomicChat/Inkling-GGUF/resolve/main/hero.png
	base_model:
	- thinkingmachines/Inkling
	base_model_relation: quantized
	quantized_by: AtomicChat
	pipeline_tag: text-generation
	library_name: gguf
	tags:
	- atomic-chat
	- inkling
	- thinkingmachines
	- gguf
	- llama.cpp
	- imatrix
	- quantized
	---

	<center>

	<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
	<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Inkling-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>
	<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Inkling-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>
	<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Inkling-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>
	</div>

	<br/>

	<img src="https://huggingface.co/AtomicChat/Inkling-GGUF/resolve/main/hero.png" alt="Inkling" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

	<div style="display:flex; justify-content:center; gap:0.5em;">
	<a href="https://huggingface.co/thinkingmachines/Inkling"><strong>Base model: thinkingmachines/Inkling</strong></a>
	</div>
	</center>

	Inkling, self-quantized to GGUF by [Atomic Chat](https://atomic.chat). Built straight from Thinking Machines Lab's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

	## Highlights

	- 952.4B parameters: the weights this repo quantizes.
	- 66 layers: Mixture-of-Experts.
	- Modalities: the base model handles Text, Image, Audio; this repo ships text-only quants, it carries no vision projector.
	- Full imatrix ladder: every quant is calibrated with an importance matrix, published here alongside the quants.

	> [!NOTE]
	> These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

	> [!IMPORTANT]
	> Always pass `--jinja` so the Inkling chat template is applied. Without it the model can emit malformed turns.

	## Model Overview

	\| Property \| Value \|
	\|---\|---\|
	\| Base model \| `thinkingmachines/Inkling` \|
	\| Parameters \| 952.4B \|
	\| Layers \| 66 \|
	\| Experts \| 256 routed (top-6) \|
	\| Context length \| not stated \|
	\| Vocabulary \| 201,024 \|
	\| Modalities \| Text, Image, Audio in the base model; text only in this repo, it ships no vision projector \|
	\| Architecture \| Mixture-of-Experts, 256 experts (top-6), 64 attention heads over 8 KV heads, `InklingForConditionalGeneration` \|
	\| This repo \| GGUF quants (imatrix); the importance matrix is published here as `imatrix/imatrix-code-at_128.gguf` \|

	<img src="https://huggingface.co/AtomicChat/Inkling-GGUF/resolve/main/benchmark.png" alt="Inkling benchmark scores" style="width:100%; max-width:900px;"/>

	Scores are Thinking Machines Lab's published results for the base `thinkingmachines/Inkling`, not our own measurements. Quantization preserves the large majority of this; `Q4_K_M` and up stay close to full precision.

	## Get started

	Run Inkling locally with:

	- [Atomic Chat](https://atomic.chat): the easiest path. Open the app, search `AtomicChat/Inkling-GGUF`, pick a quant, hit Use this model.
	- llama.cpp: `llama-server -hf AtomicChat/Inkling-GGUF:None --jinja -c 8192`
	- Ollama: `ollama run hf.co/AtomicChat/Inkling-GGUF:None`
	- LM Studio / Jan: search the repo id, download any quant.

	## Best practices

	\| Parameter \| Value \|
	\|---\|---\|
	\| sampling defaults \| not stated \|

	The base model card does not state sampling defaults.

	## Run in llama.cpp

	```bash
	git clone https://github.com/ggml-org/llama.cpp
	cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
	cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server
	```

	```bash
	./llama.cpp/build/bin/llama-server \
	-hf AtomicChat/Inkling-GGUF:None \
	--jinja -ngl 99 -c 8192 -fa on
	```

	## How these were made

	1. Download `thinkingmachines/Inkling` (original weights).
	2. Convert to f16 GGUF with [llama.cpp](https://github.com/ggml-org/llama.cpp).
	3. Build an importance matrix over our calibration corpus, published here as `imatrix/imatrix-code-at_128.gguf`.
	4. Quantize the ladder with `--imatrix`.

	## License

	Original model by Thinking Machines Lab, released under the Apache 2.0 license. Full terms: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). Quantized by Atomic Chat.