---

base_model: bottlecapai/ThinkingCap-Qwen3.6-27B

library_name: gguf

tags:

- qwen3_6

- token-efficient

- efficient-thinking

- gguf

- mtp

- multi-token-prediction

- llama-cpp

---

<a href="https://www.bottlecapai.com/"><img src="https://raw.githubusercontent.com/bottlecapai/assets/main/bottlecap-logo.png" alt="BottleCap AI" width="300"></a>

ThinkingCap: Qwen 3.6 27B - MTP GGUF

This is a GGUF quantized version of bottlecapai/ThinkingCap-Qwen3.6-27B customized with MTP (Multi-Token Prediction) support. This repository contains the Q4_K_M quantization, providing a balanced trade-off between memory efficiency and reasoning quality for local inference. By integrating MTP, this specific GGUF model not only thinks with fewer tokens but also generates them significantly faster in compatible runtimes.

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⚡ MTP (Multi-Token Prediction) Support

This model is compiled with MTP routing. MTP allows the model to predict multiple future tokens simultaneously during inference rather than one by one. When paired with ThinkingCap's already reduced reasoning trace lengths, this results in blistering fast time-to-first-answer and overall generation speeds on local hardware.

Running with llama.cpp

To take advantage of MTP, ensure you are using a recent build of llama.cpp that supports Qwen's multi-token routing.

(Note: Depending on your specific llama.cpp version, MTP may be enabled by default for supported architectures, or you may need to pass specific draft/speculative flags).

# Basic run command
./llama-cli -hf Abiray/ThinkingCap-Qwen3.6-27B-MTP-Q4_K_M-GGUF -p "Explain quantum computing in simple terms."