Instructions to use baselquant/Inkling-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 baselquant/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 baselquant/Inkling-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf baselquant/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 baselquant/Inkling-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf baselquant/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 baselquant/Inkling-GGUF:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf baselquant/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 baselquant/Inkling-GGUF:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf baselquant/Inkling-GGUF:IQ1_M

Use Docker

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

LM Studio
Jan

vLLM

How to use baselquant/Inkling-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

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

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

Unsloth Studio

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

How to use baselquant/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 baselquant/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": "baselquant/Inkling-GGUF:IQ1_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

Atomic Chat new

OpenClaw new

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

Lemonade

How to use baselquant/Inkling-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Inkling-GGUF-IQ1_M

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Base model: thinkingmachines/Inkling

Inkling, self-quantized to GGUF by 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.

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.

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`

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: 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

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

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

How these were made

Download thinkingmachines/Inkling (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus, published here as imatrix/imatrix-code-at_128.gguf.
Quantize the ladder with --imatrix.

License

Original model by Thinking Machines Lab, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.

Downloads last month: -

GGUF

Model size

947B params

Architecture

inkling

Hardware compatibility

1-bit

8-bit

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