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yvfu/Qwen3-Coder-30B-A3B-Instruct-Q6_K-GGUF overview

yvfu/Qwen3 Coder 30B A3B Instruct Q6 K GGUF This model was converted to GGUF format from Qwen/Qwen3 Coder 30B A3B Instruct https://huggingface.co/Qwen/Qwen3 Co…

transformersggufllama-cppgguf-my-repotext-generationbase_model:Qwen/Qwen3-Coder-30B-A3B-Instructbase_model:quantized:Qwen/Qwen3-Coder-30B-A3B-Instructlicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~23.37 GB disk (24 GB VRAM class GPUs with llama.cpp / guIDE).

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text-generation
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1 GGUF files detected
Direct downloads for local inference
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qwen3-coder-30b-a3b-instruct-q6_k.ggufGGUFQ6_K23.37 GBDownload

Model Details

Model IDyvfu/Qwen3-Coder-30B-A3B-Instruct-Q6_K-GGUF
Authoryvfu
Pipelinetext-generation
Licenseapache-2.0
Base modelQwen/Qwen3-Coder-30B-A3B-Instruct
Last modified2026-07-08T10:56:14.000Z

Model README

---

library_name: transformers

license: apache-2.0

license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE

pipeline_tag: text-generation

base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct

tags:

  • llama-cpp
  • gguf-my-repo

---

yvfu/Qwen3-Coder-30B-A3B-Instruct-Q6_K-GGUF

This model was converted to GGUF format from Qwen/Qwen3-Coder-30B-A3B-Instruct 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 yvfu/Qwen3-Coder-30B-A3B-Instruct-Q6_K-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo yvfu/Qwen3-Coder-30B-A3B-Instruct-Q6_K-GGUF --hf-file qwen3-coder-30b-a3b-instruct-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 yvfu/Qwen3-Coder-30B-A3B-Instruct-Q6_K-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo yvfu/Qwen3-Coder-30B-A3B-Instruct-Q6_K-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q6_k.gguf -c 2048

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