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yangthecoder/Qwen3.6-35B-A3B-FP8-Q4_K_M-GGUF overview

yangthecoder/Qwen3.6 35B A3B FP8 Q4 K M GGUF This model was converted to GGUF format from Qwen/Qwen3.6 35B A3B FP8 https://huggingface.co/Qwen/Qwen3.6 35B A3B …

transformersggufllama-cppgguf-my-repoimage-text-to-textbase_model:Qwen/Qwen3.6-35B-A3B-FP8base_model:quantized:Qwen/Qwen3.6-35B-A3B-FP8license:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~10.4 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
image-text-to-text

Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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qwen3.6-35b-a3b-fp8-q4_k_m.ggufGGUFQ4_K_M10.4 MBDownload

Model Details

Model IDyangthecoder/Qwen3.6-35B-A3B-FP8-Q4_K_M-GGUF
Authoryangthecoder
Pipelineimage-text-to-text
Licenseapache-2.0
Base modelQwen/Qwen3.6-35B-A3B-FP8
Last modified2026-06-18T10:51:38.000Z

Model README

---

base_model: Qwen/Qwen3.6-35B-A3B-FP8

frameworks:

  • ''

library_name: transformers

license: apache-2.0

license_link: https://huggingface.co/Qwen/Qwen3.6-35B-A3B-FP8/blob/main/LICENSE

pipeline_tag: image-text-to-text

tasks: []

tags:

  • llama-cpp
  • gguf-my-repo

---

yangthecoder/Qwen3.6-35B-A3B-FP8-Q4_K_M-GGUF

This model was converted to GGUF format from Qwen/Qwen3.6-35B-A3B-FP8 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 yangthecoder/Qwen3.6-35B-A3B-FP8-Q4_K_M-GGUF --hf-file qwen3.6-35b-a3b-fp8-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo yangthecoder/Qwen3.6-35B-A3B-FP8-Q4_K_M-GGUF --hf-file qwen3.6-35b-a3b-fp8-q4_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 yangthecoder/Qwen3.6-35B-A3B-FP8-Q4_K_M-GGUF --hf-file qwen3.6-35b-a3b-fp8-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo yangthecoder/Qwen3.6-35B-A3B-FP8-Q4_K_M-GGUF --hf-file qwen3.6-35b-a3b-fp8-q4_k_m.gguf -c 2048

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