license: apache-2.0
base_model: hotdogs/Qwen3.8-27B-abliterated-AWQ-INT4
base_model_relation: quantized
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3_5
- awq
- gptq
- compressed-tensors
- w4a16
- int4
- quantized
- abliterated
- uncensored
- conversational
- vllm
- llama-cpp
- gguf-my-repo
language: - en
- zh
- th
MangoMikePower/Qwen3.8-27B-abliterated-AWQ-INT4-Q4_K_M-GGUF
This model was converted to GGUF format from hotdogs/Qwen3.8-27B-abliterated-AWQ-INT4 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/Qwen3.8-27B-abliterated-AWQ-INT4-Q4_K_M-GGUF --hf-file qwen3.8-27b-abliterated-awq-int4-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo MangoMikePower/Qwen3.8-27B-abliterated-AWQ-INT4-Q4_K_M-GGUF --hf-file qwen3.8-27b-abliterated-awq-int4-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/Qwen3.8-27B-abliterated-AWQ-INT4-Q4_K_M-GGUF --hf-file qwen3.8-27b-abliterated-awq-int4-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo MangoMikePower/Qwen3.8-27B-abliterated-AWQ-INT4-Q4_K_M-GGUF --hf-file qwen3.8-27b-abliterated-awq-int4-q4_k_m.gguf -c 2048