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OliviaRossi/KAT-Ornith-Coder-35B-A3B-V2-Q5_K_M-GGUF overview

OliviaRossi/KAT Ornith Coder 35B A3B V2 Q5 K M GGUF This model was converted to GGUF format from OliviaRossi/KAT Ornith Coder 35B A3B V2 https://huggingface.co…

ggufmergegeometric-slerpagentic-codingswe-benchmoellama-cppgguf-my-repobase_model:OliviaRossi/KAT-Ornith-Coder-35B-A3B-V2base_model:quantized:OliviaRossi/KAT-Ornith-Coder-35B-A3B-V2license:apache-2.0endpoints_compatibleregion:usconversational

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

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1 GGUF files detected
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kat-ornith-coder-35b-a3b-v2-q5_k_m.ggufGGUFQ5_K_M23.03 GBDownload

Model Details

Model IDOliviaRossi/KAT-Ornith-Coder-35B-A3B-V2-Q5_K_M-GGUF
AuthorOliviaRossi
Pipeline
Licenseapache-2.0
Base modelOliviaRossi/KAT-Ornith-Coder-35B-A3B-V2
Last modified2026-09-10T04:16:37.000Z

Model README

---

license: apache-2.0

base_model: OliviaRossi/KAT-Ornith-Coder-35B-A3B-V2

tags:

  • merge
  • geometric-slerp
  • agentic-coding
  • swe-bench
  • moe
  • llama-cpp
  • gguf-my-repo

---

OliviaRossi/KAT-Ornith-Coder-35B-A3B-V2-Q5_K_M-GGUF

This model was converted to GGUF format from OliviaRossi/KAT-Ornith-Coder-35B-A3B-V2 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/KAT-Ornith-Coder-35B-A3B-V2-Q5_K_M-GGUF --hf-file kat-ornith-coder-35b-a3b-v2-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo OliviaRossi/KAT-Ornith-Coder-35B-A3B-V2-Q5_K_M-GGUF --hf-file kat-ornith-coder-35b-a3b-v2-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/KAT-Ornith-Coder-35B-A3B-V2-Q5_K_M-GGUF --hf-file kat-ornith-coder-35b-a3b-v2-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo OliviaRossi/KAT-Ornith-Coder-35B-A3B-V2-Q5_K_M-GGUF --hf-file kat-ornith-coder-35b-a3b-v2-q5_k_m.gguf -c 2048

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