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MangoMikePower/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16-Q4_K_M-GGUF overview

MangoMikePower/Swift Qwen3.8 27B Uncensored GPTQ Int4 sym G128 MTP BF16 Q4 K M GGUF This model was converted to GGUF format from greglechin/Swift Qwen3.8 27B U…

transformersggufgptqint4uncensoredabliteratedmtpspeculative-decodingintelarcintel-arcxpuvllmqwen3.8swiftllama-cppgguf-my-repoimage-text-to-textenzhbase_model:greglechin/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16base_model:quantized:greglechin/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16license:otherendpoints_compatible

Runs locally from ~15.66 GB disk (16 GB VRAM class GPUs with llama.cpp / guIDE).

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image-text-to-text

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1 GGUF files detected
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swift-qwen3.8-27b-uncensored-gptq-int4-sym-g128-mtp-bf16-q4_k_m.ggufGGUFBF1615.66 GBDownload

Model Details

Model IDMangoMikePower/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16-Q4_K_M-GGUF
AuthorMangoMikePower
Pipelineimage-text-to-text
Licenseother
Base modelgreglechin/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16
Last modified2026-09-18T19:05:34.000Z

Model README

---

license: other

license_name: swift-open-license-1.0

license_link: https://ukisai.com/contact

base_model: greglechin/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16

base_model_relation: quantized

library_name: transformers

language:

  • en
  • zh

pipeline_tag: image-text-to-text

tags:

  • gptq
  • int4
  • uncensored
  • abliterated
  • mtp
  • speculative-decoding
  • intel
  • arc
  • intel-arc
  • xpu
  • vllm
  • qwen3.8
  • swift
  • llama-cpp
  • gguf-my-repo

---

MangoMikePower/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16-Q4_K_M-GGUF

This model was converted to GGUF format from greglechin/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16 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 MangoMikePower/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16-Q4_K_M-GGUF --hf-file swift-qwen3.8-27b-uncensored-gptq-int4-sym-g128-mtp-bf16-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo MangoMikePower/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16-Q4_K_M-GGUF --hf-file swift-qwen3.8-27b-uncensored-gptq-int4-sym-g128-mtp-bf16-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 MangoMikePower/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16-Q4_K_M-GGUF --hf-file swift-qwen3.8-27b-uncensored-gptq-int4-sym-g128-mtp-bf16-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo MangoMikePower/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16-Q4_K_M-GGUF --hf-file swift-qwen3.8-27b-uncensored-gptq-int4-sym-g128-mtp-bf16-q4_k_m.gguf -c 2048

Run MangoMikePower/Swift-Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16-Q4_K_M-GGUF with guIDE

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