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tripla-m/Ministral-3-14B-Base-2512-Q6_K-GGUF overview

tripla m/Ministral 3 14B Base 2512 Q6 K GGUF This model was converted to GGUF format from mistralai/Ministral 3 14B Base 2512 https://huggingface.co/mistralai/…

vllmggufmistral-commonllama-cppgguf-my-repoenfresdeitptnlzhjakoarbase_model:mistralai/Ministral-3-14B-Base-2512base_model:quantized:mistralai/Ministral-3-14B-Base-2512license:apache-2.0region:us

Runs locally from ~10.33 GB disk (12 GB VRAM class GPUs with llama.cpp / guIDE).

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1 GGUF files detected
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ministral-3-14b-base-2512-q6_k.ggufGGUFQ6_K10.33 GBDownload

Model Details

Model IDtripla-m/Ministral-3-14B-Base-2512-Q6_K-GGUF
Authortripla-m
Pipeline
Licenseapache-2.0
Base modelmistralai/Ministral-3-14B-Base-2512
Last modified2026-07-10T09:11:18.000Z

Model README

---

library_name: vllm

language:

  • en
  • fr
  • es
  • de
  • it
  • pt
  • nl
  • zh
  • ja
  • ko
  • ar

license: apache-2.0

inference: false

extra_gated_description: If you want to learn more about how we process your personal

data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.

tags:

  • mistral-common
  • llama-cpp
  • gguf-my-repo

base_model: mistralai/Ministral-3-14B-Base-2512

---

tripla-m/Ministral-3-14B-Base-2512-Q6_K-GGUF

This model was converted to GGUF format from mistralai/Ministral-3-14B-Base-2512 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 tripla-m/Ministral-3-14B-Base-2512-Q6_K-GGUF --hf-file ministral-3-14b-base-2512-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo tripla-m/Ministral-3-14B-Base-2512-Q6_K-GGUF --hf-file ministral-3-14b-base-2512-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 tripla-m/Ministral-3-14B-Base-2512-Q6_K-GGUF --hf-file ministral-3-14b-base-2512-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo tripla-m/Ministral-3-14B-Base-2512-Q6_K-GGUF --hf-file ministral-3-14b-base-2512-q6_k.gguf -c 2048

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