---
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
---
**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` |
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.
