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2448FT/Qwen3-8B-DE-Swap-Q4_K_M-GGUF overview

2448FT/Qwen3 8B DE Swap Q4 K M GGUF This model was converted to GGUF format from lightonai/Qwen3 8B DE Swap https://huggingface.co/lightonai/Qwen3 8B DE Swap u…

transformersggufmultilingualreasoningLLMqwen3layer-swapllama-cppgguf-my-repotext-generationdedataset:lightonai/Dolci-Think-SFT-32B-Multilingualbase_model:lightonai/Qwen3-8B-DE-Swapbase_model:quantized:lightonai/Qwen3-8B-DE-Swaplicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~4.68 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

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text-generation
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1 GGUF files detected
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qwen3-8b-de-swap-q4_k_m.ggufGGUFQ4_K_M4.68 GBDownload

Model Details

Model ID2448FT/Qwen3-8B-DE-Swap-Q4_K_M-GGUF
Author2448FT
Pipelinetext-generation
Licenseapache-2.0
Base modellightonai/Qwen3-8B-DE-Swap
Last modified2026-08-30T04:37:31.000Z

Model README

---

library_name: transformers

base_model: lightonai/Qwen3-8B-DE-Swap

tags:

  • multilingual
  • reasoning
  • LLM
  • qwen3
  • layer-swap
  • llama-cpp
  • gguf-my-repo

license: apache-2.0

datasets:

  • lightonai/Dolci-Think-SFT-32B-Multilingual

language:

  • de

pipeline_tag: text-generation

---

2448FT/Qwen3-8B-DE-Swap-Q4_K_M-GGUF

This model was converted to GGUF format from lightonai/Qwen3-8B-DE-Swap 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 2448FT/Qwen3-8B-DE-Swap-Q4_K_M-GGUF --hf-file qwen3-8b-de-swap-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo 2448FT/Qwen3-8B-DE-Swap-Q4_K_M-GGUF --hf-file qwen3-8b-de-swap-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 2448FT/Qwen3-8B-DE-Swap-Q4_K_M-GGUF --hf-file qwen3-8b-de-swap-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo 2448FT/Qwen3-8B-DE-Swap-Q4_K_M-GGUF --hf-file qwen3-8b-de-swap-q4_k_m.gguf -c 2048

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