license: apache-2.0 language:

• ko
• en library_name: llama.cpp pipeline_tag: text-generation inference: false base_model:
• huihui-ai/Huihui-Qwen3.8-27B-Coder390-EfficientThink-abliterated-GGUF base_model_relation: quantized tags:
• gguf
• qwen3.8
• korean
• coding
• imatrix
• mixed-precision
• mtp

Coder390 EfficientThink Abliterated · KO Ridge 3.7bpw GGUF

A Korean-calibrated mixed-precision GGUF of Huihui Qwen3.8 27B Coder390 EfficientThink Abliterated, quantized directly from the upstream BF16 GGUF.

이 모델은 Coder390 EfficientThink Abliterated의 BF16 가중치를 한국어 보정 데이터와 Ridge 방식의 혼합 정밀도로 양자화한 GGUF입니다. 한국어로 추가 학습한 모델은 아니며, 한국어 품질 향상을 별도 벤치마크로 검증하지 않았습니다.

Model and provenance

• BF16 source: huihui-ai/Huihui-Qwen3.8-27B-Coder390-EfficientThink-abliterated-GGUF, revision a0b17c71d9943529aefe88b83f494bf01c6b4d68.
• Source filename: Huihui-Qwen3.8-27B-Coder390-EfficientThink-abliterated-bf16.gguf.
• Source SHA-256: fcddc1ff6efa4932cf4a7eb18087be39527b740cdca0810bbd3a717312d61d53.
• Calibration corpus: augustine223/korean-imatrix-calibration-corpus, revision 4ff695275c950f4311e84c561883566c8ab874dd.
• Corpus SHA-256: bca66acae75db6a9a1d4ad152329b2c31dd235f2fc73dbb93f863d4d7b402b3c.
• The importance matrix was newly collected on a Coder390 Q8_0 calibration copy, using 708 chunks of 512 tokens. The final model was quantized from BF16 using that matrix.
• Tensor precision allocation follows empero-ai/Qwen3.8-27B-Ridge-GGUF; the quantizer was pinned to llama.cpp commit adb55e5.
• The embedded tokenizer, chat template, and MTP tensors are preserved. This repository contains the text GGUF; no vision projector or external draft model is included.
• The upstream model is licensed under Apache 2.0; see LICENSE. Original model attribution is retained above. This derivative changes quantization and removes local calibration paths from metadata.

Tensor types

The file is approximately 12.60 GB (11.73 GiB), about 3.69 bits per parameter. Runtime memory also includes the KV cache and compute buffers.

Download and run

Use a llama.cpp build that supports the qwen35 architecture and these GGUF quantization types.

hf download donghanasd/Huihui-Qwen3.8-27B-Coder390-EfficientThink-abliterated-KO-Ridge-3.7bpw-GGUF \
  Huihui-Qwen3.8-27B-Coder390-EfficientThink-abliterated-KO-Ridge-3.7bpw.gguf \
  --local-dir .

llama-server \
  --model Huihui-Qwen3.8-27B-Coder390-EfficientThink-abliterated-KO-Ridge-3.7bpw.gguf \
  --host 127.0.0.1 --port 8080 \
  --jinja --no-mmproj --gpu-layers 99 --ctx-size 32768

The starting context above is 32K. Reduce the context or GPU layer count if memory is insufficient. The GGUF declares a 262,144-token context; that metadata does not guarantee that your hardware can run that context length. No speed or quality benchmark is claimed for this upload.

File integrity

Exact size: 12599186976 bytes.

SHA-256:

47de712523d6fbfede267d01739a6282f39724d1325987208693a33f75f5e353