MangoMikePower/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70-Q4_K_M-GGUF overview
MangoMikePower/Huihui Qwen3.8 27B abliterated GPTQ Int4 sym G128 MTP BF16 B70 Q4 K M GGUF This model was converted to GGUF format from zrlu/Huihui Qwen3.8 27B …
Runs locally from ~15.66 GB disk (16 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| huihui-qwen3.8-27b-abliterated-gptq-int4-sym-g128-mtp-bf16-b70-q4_k_m.gguf | GGUF | BF16 | 15.66 GB | Download |
Model Details
| Model ID | MangoMikePower/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70-Q4_K_M-GGUF |
|---|---|
| Author | MangoMikePower |
| Pipeline | text-generation |
| License | apache-2.0 |
| Base model | zrlu/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70 |
| Last modified | 2026-09-18T19:53:29.000Z |
Model README
---
license: apache-2.0
language:
- en
- zh
library_name: transformers
pipeline_tag: text-generation
base_model: zrlu/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70
tags:
- qwen3.8
- abliterated
- uncensored
- gptq
- int4
- vllm
- xpu
- intel-arc
- arc-pro-b70
- b70
- quantization
- speculative-decoding
- vision
- llama-cpp
- gguf-my-repo
quantized_by: zrlu
---
MangoMikePower/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70-Q4_K_M-GGUF
This model was converted to GGUF format from zrlu/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70 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/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70-Q4_K_M-GGUF --hf-file huihui-qwen3.8-27b-abliterated-gptq-int4-sym-g128-mtp-bf16-b70-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo MangoMikePower/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70-Q4_K_M-GGUF --hf-file huihui-qwen3.8-27b-abliterated-gptq-int4-sym-g128-mtp-bf16-b70-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/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70-Q4_K_M-GGUF --hf-file huihui-qwen3.8-27b-abliterated-gptq-int4-sym-g128-mtp-bf16-b70-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo MangoMikePower/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70-Q4_K_M-GGUF --hf-file huihui-qwen3.8-27b-abliterated-gptq-int4-sym-g128-mtp-bf16-b70-q4_k_m.gguf -c 2048Run MangoMikePower/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70-Q4_K_M-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models