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c41n3/GLM-4.7-Flash-abliteratex-Q6_K-GGUF overview

c41n3/GLM 4.7 Flash abliteratex Q6 K GGUF This model was converted to GGUF format from wangzhang/GLM 4.7 Flash abliteratex https://huggingface.co/wangzhang/GLM…

transformersggufglmmoeabliteratedllama-cppgguf-my-repotext-generationenzhdataset:wangzhang/abliterix-datasetsbase_model:wangzhang/GLM-4.7-Flash-abliteratexbase_model:quantized:wangzhang/GLM-4.7-Flash-abliteratexlicense:mitendpoints_compatibleregion:us

Runs locally from ~22.92 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
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glm-4.7-flash-abliteratex-q6_k.ggufGGUFQ6_K22.92 GBDownload

Model Details

Model IDc41n3/GLM-4.7-Flash-abliteratex-Q6_K-GGUF
Authorc41n3
Pipelinetext-generation
Licensemit
Base modelwangzhang/GLM-4.7-Flash-abliteratex
Last modified2026-10-04T15:05:23.000Z

Model README

---

license: mit

license_link: LICENSE

base_model: wangzhang/GLM-4.7-Flash-abliteratex

datasets:

  • wangzhang/abliterix-datasets

language:

  • en
  • zh

library_name: transformers

pipeline_tag: text-generation

tags:

  • glm
  • moe
  • abliterated
  • gguf
  • llama-cpp
  • gguf-my-repo

---

c41n3/GLM-4.7-Flash-abliteratex-Q6_K-GGUF

This model was converted to GGUF format from wangzhang/GLM-4.7-Flash-abliteratex 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 c41n3/GLM-4.7-Flash-abliteratex-Q6_K-GGUF --hf-file glm-4.7-flash-abliteratex-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo c41n3/GLM-4.7-Flash-abliteratex-Q6_K-GGUF --hf-file glm-4.7-flash-abliteratex-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 c41n3/GLM-4.7-Flash-abliteratex-Q6_K-GGUF --hf-file glm-4.7-flash-abliteratex-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo c41n3/GLM-4.7-Flash-abliteratex-Q6_K-GGUF --hf-file glm-4.7-flash-abliteratex-q6_k.gguf -c 2048

Run c41n3/GLM-4.7-Flash-abliteratex-Q6_K-GGUF with guIDE

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