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gopi87/ThinkingCap-Qwen3.6-27B-Q6_K-GGUF overview

gopi87/ThinkingCap Qwen3.6 27B Q6 K GGUF This model was converted to GGUF format from bottlecapai/ThinkingCap Qwen3.6 27B https://huggingface.co/bottlecapai/Th…

Image-Text-to-Textggufqwen3_6token-efficientefficient-thinkingllama-cppgguf-my-repounslothqwenqwen3_5base_model:bottlecapai/ThinkingCap-Qwen3.6-27Bbase_model:quantized:bottlecapai/ThinkingCap-Qwen3.6-27Bendpoints_compatibleregion:us

Runs locally from ~888.0 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

2 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
mmproj-BF16.ggufGGUFBF16888.0 MBDownload
thinkingcap-qwen3.6-27b-q6_k.ggufGGUFQ6_K20.89 GBDownload

Model Details

Model IDgopi87/ThinkingCap-Qwen3.6-27B-Q6_K-GGUF
Authorgopi87
Pipeline
License
Base modelbottlecapai/ThinkingCap-Qwen3.6-27B
Last modified2026-07-10T12:12:08.000Z

Model README

---

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

library_name: Image-Text-to-Text

tags:

  • qwen3_6
  • token-efficient
  • efficient-thinking
  • llama-cpp
  • gguf-my-repo
  • unsloth
  • qwen
  • qwen3_5

---

gopi87/ThinkingCap-Qwen3.6-27B-Q6_K-GGUF

This model was converted to GGUF format from bottlecapai/ThinkingCap-Qwen3.6-27B using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo gopi87/ThinkingCap-Qwen3.6-27B-Q6_K-GGUF --hf-file thinkingcap-qwen3.6-27b-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo gopi87/ThinkingCap-Qwen3.6-27B-Q6_K-GGUF --hf-file thinkingcap-qwen3.6-27b-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo gopi87/ThinkingCap-Qwen3.6-27B-Q6_K-GGUF --hf-file thinkingcap-qwen3.6-27b-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo gopi87/ThinkingCap-Qwen3.6-27B-Q6_K-GGUF --hf-file thinkingcap-qwen3.6-27b-q6_k.gguf -c 2048

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