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AtomicChat/Inkling-GGUF

🤗 On Hugging Facetext-generationapache-2.01.8 TBGGUF✓ HF checksums availableupdated today
Magnet

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.
[!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: 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

1. Download thinkingmachines/Inkling (original weights).

2. Convert to f16 GGUF with 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. Quantized by Atomic Chat.