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Y0us/Medina-Qwen3.5-27B-OpenClaw-Uncensored-GGUF

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Model Card

Medina-Qwen3.5-27B-OpenClaw-uncensored — GGUF

GGUF quantizations of `Y0us/Medina-Qwen3.5-27B-OpenClaw-uncensored`, an abliterated variant of `peterjohannmedina/Medina-Qwen3.5-27B-OpenClaw`.

For full methodology and evaluation details, see the main model card.

GGUF Downloads

Quantization	Size	Use case
F16	51 GB	Reference / re-quantization source
Q4_K_M	16 GB	✅ Recommended — 24 GB VRAM or 32 GB unified memory

Abliteration Summary

Parameter	Value
Base	Medina-Qwen3.5-27B-OpenClaw (base + LoRA merged to BF16)
Method	Refusal-direction projection
Target weights	attention `o_proj`, linear-attention `out_proj`, MLP `down_proj`
Layers modified	40 (top by refusal contribution)
Refusal scope	English + Korean patterns

Evaluation

Numbers below are from the Q4_K_M build in this repo. All runs used llama.cpp with --parallel 1 --cache-reuse 0, temperature=0.0, greedy decoding.

Refusal rate (`mlabonne/harmful_behaviors`, N=50)

Model	Refusals	Rate
Original `Medina-Qwen3.5-27B-OpenClaw`	50 / 50	100.0%
This model	0 / 50	0.0%

Capability preservation

Benchmark	Original	Uncensored	Δ
MMLU High School Computer Science	93.33%	96.67%	+3.33
MMLU College Mathematics	93.33%	93.33%	0.00
MMLU Formal Logic	96.67%	96.67%	0.00
MMLU Professional Law	83.33%	76.67%	−6.67
MMLU Moral Scenarios	73.33%	76.67%	+3.33
MMLU Overall (150 Q)	88.00%	88.00%	0.00
GSM8K (50 Q)	98.00%	98.00%	0.00

MMLU and GSM8K run 0-shot in generation mode (CoT + Answer: X). Overall MMLU and GSM8K are identical (132/150 and 49/50).

Usage with llama.cpp

bash

./llama-server \
  -m Medina-Qwen3.5-27B-OpenClaw-uncensored-Q4_K_M.gguf \
  --port 8080 --host 127.0.0.1 \
  -c 8192 -ngl 999 \
  --chat-template chatml \
  --parallel 1 \
  --cache-reuse 0

Important flags for this architecture:

—--parallel 1 — the base uses hybrid GatedDeltaNet + attention; parallel slots thrash the cache.
—--cache-reuse 0 — KV cache cannot currently be reused across turns for this architecture under llama.cpp.

See llama.cpp PR #13194 for background.

Usage with Ollama

bash

# Direct from this repo (after quantization is hosted)
ollama run hf.co/Y0us/Medina-Qwen3.5-27B-OpenClaw-uncensored-GGUF:Q4_K_M

Known Limitations

—Hybrid architecture prevents KV-cache reuse across turns under current llama.cpp — every prompt is fully re-processed (runtime limitation, not a weight issue).
—Refusal benchmark (N=50) is a sanity check, not an exhaustive safety audit.
—Professional Law shows a −6.67 point drop on MMLU (N=30).

License

Apache 2.0 — same as the base model.