Text Generation
GGUF
atomic-chat
inkling
thinkingmachines
llama.cpp
imatrix
quantized
conversational
Instructions to use AtomicChat/Inkling-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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
- Pi
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
File size: 4,880 Bytes
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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.
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