The base model is a Claude 4.6 Opus reasoning distillation of Qwen3.5-27B, fine-tuned on OpenClaw tool-call data. This variant removes the refusal direction from the merged weights while preserving the base's tool-calling capability and general reasoning performance.

Downloads

This repository contains the full merged BF16 weights in transformers sharded format (~54 GB). For GGUF quantizations, see the companion repo:

GGUF quantizations → Medina-Qwen3.5-27B-OpenClaw-Uncensored-GGUF

Abliteration Details

Parameter	Value
Source	Medina-Qwen3.5-27B-OpenClaw (base + LoRA, merged to BF16)
GPU	NVIDIA GB10 (128 GB unified)
Method	Refusal-direction projection (single orthogonal direction)
Target weights	attention `o_proj`, linear-attention `out_proj`, MLP `down_proj`
Layer selection	top 40 layers by refusal contribution (TOP_K_CAP=40)
Train / val split	N_TRAIN=48 / N_VAL=20
Winsorization	q=0.995
Orthogonalization	`r_pure = r_raw − (r_raw · c_unit) * c_unit`
Refusal scope	English + Korean patterns
Quantization	BF16 (full precision merge)

The projected refusal direction is orthogonal to the compliance direction, so refusal behavior is removed without perturbing the compliance-related components of the weights.

Evaluation

All numbers from the Q4_K_M GGUF build running under llama.cpp with --parallel 1 --cache-reuse 0, temperature=0.0, greedy decoding. MMLU and GSM8K were run in generation mode (0-shot CoT + Answer: X), not loglikelihood.

Refusal rate — `mlabonne/harmful_behaviors`, test split, N=50

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

Capability preservation — MMLU (gen, N=30 per subject) + GSM8K (N=50)

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 overall and GSM8K are identical (132/150 and 49/50 respectively in both models). The only material per-subject change is Professional Law (−6.67), a known side-effect of refusal-direction projection where legal-judgment reasoning shares structural features with the refusal direction. Given N=30, the true effect size is likely smaller than it appears.

Usage with Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Y0us/Medina-Qwen3.5-27B-OpenClaw-Uncensored"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Your prompt here"}]
inputs = tok.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
).to(model.device)
out = model.generate(inputs, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Tool-calling uses the same OpenClaw XML format as the base model. See the base model card for the tool-call schema.

Known Limitations

Hybrid architecture — The base uses GatedDeltaNet + attention. Under current llama.cpp, KV cache cannot be reused across turns; every prompt is fully re-processed. See llama.cpp PR #13194.
Small eval set — N=50 refusal and N=30 per MMLU subject. This is a sanity check, not an exhaustive safety audit.
Professional Law drop — Measurable −6.67 on N=30; evaluate for your legal-reasoning use cases.
Partial coverage — The projection targets a single empirically-estimated refusal direction. Out-of-distribution prompts, multi-turn jailbreak chains, or culturally specific refusal patterns may still elicit refusals.

Intended Use

Released for research on refusal mechanisms, red-teaming and evaluation work, and capability-retention studies on abliteration. Users are responsible for downstream use and must comply with applicable laws and the upstream base-model license.

Acknowledgments

Base model: peterjohannmedina/Medina-Qwen3.5-27B-OpenClaw (LoRA fine-tune of Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled)
Abliteration technique inspired by refusal-direction projection literature on decoder-only transformers
Benchmarks: cais/mmlu, gsm8k, mlabonne/harmful_behaviors

License

Apache 2.0 — same as the base model.

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

Medina-Qwen3.5-27B-OpenClaw-Uncensored