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OliviaRossi/QuadQwen-Q5_K_M-GGUF overview

license: apache 2.0 language: en zh tags: moe qwen qwen3 qwen3.5 qwen3.6 code coding agent tool calling reasoning thinking gated deltanet hybrid attention merg…

ggufmoeqwenqwen3qwen3.5qwen3.6codecodingagenttool-callingreasoningthinkinggated-deltanethybrid-attentionmergedare-tiesslerpllama-cppgguf-my-repotext-generationenzhbase_model:OliviaRossi/QuadQwenbase_model:quantized:OliviaRossi/QuadQwen

Runs locally from ~23.03 GB disk (24 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation

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1 GGUF files detected
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Model Details

Model IDOliviaRossi/QuadQwen-Q5_K_M-GGUF
AuthorOliviaRossi
Pipelinetext-generation
Licenseapache-2.0
Base modelOliviaRossi/QuadQwen
Last modified2026-09-04T21:20:05.000Z

Model README

---

license: apache-2.0

language:

  • en
  • zh

tags:

  • moe
  • qwen
  • qwen3
  • qwen3.5
  • qwen3.6
  • code
  • coding
  • agent
  • tool-calling
  • reasoning
  • thinking
  • gated-deltanet
  • hybrid-attention
  • merge
  • dare-ties
  • slerp
  • llama-cpp
  • gguf-my-repo

base_model: OliviaRossi/QuadQwen

pipeline_tag: text-generation

inference: false

model_creator: Olivia Rossi

model_name: QuadQwen

---

OliviaRossi/QuadQwen-Q5_K_M-GGUF

This model was converted to GGUF format from OliviaRossi/QuadQwen 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 OliviaRossi/QuadQwen-Q5_K_M-GGUF --hf-file quadqwen-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo OliviaRossi/QuadQwen-Q5_K_M-GGUF --hf-file quadqwen-q5_k_m.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 OliviaRossi/QuadQwen-Q5_K_M-GGUF --hf-file quadqwen-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo OliviaRossi/QuadQwen-Q5_K_M-GGUF --hf-file quadqwen-q5_k_m.gguf -c 2048

Run OliviaRossi/QuadQwen-Q5_K_M-GGUF with guIDE

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