README.md

metadata

base_model: Qwen/Qwen3.6-27B
tags:
  - text-generation
  - lora
  - peft
  - svd
  - weight-diff
  - qwen3.5
  - qwen3.6
  - uncensored
  - abliterated
license: apache-2.0
language:
  - en
  - zh
pipeline_tag: text-generation

ThinkingCap Qwen3.6-27B LoRA (SVD Weight-Diff Extraction)

LoRA adapter extracted via weight-diff SVD from:

Role	Model
Source (fine)	huihui-ai/Huihui-ThinkingCap-Qwen3.6-27B-abliterated
Base	Qwen/Qwen3.6-27B

The delta W_fine - W_base is compressed with randomized SVD (rank 16), producing a standard PEFT LoRA (lora_A/lora_B, BF16). Applying it to the base model reproduces the ThinkingCap + abliteration behavior change without any training data.

Target Modules

Qwen3.6-27B is a hybrid qwen3_5 model (64 layers, Qwen3_5ForConditionalGeneration):

• self_attn.q_proj/k_proj/v_proj/o_proj — 16 full-attention layers (3, 7, 11, ..., 63)
• mlp.gate_proj/up_proj/down_proj — all 64 layers
• Linear-attention tensors (linear_attn.*) are intentionally not targeted (not compatible with standard PEFT/llama.cpp LoRA)

Quality (rank 16)

Family	count	avg |Δ|/base	energy captured
down_proj	64	1.8e-2	99.2%
o_proj	16	1.9e-2	99.3%
gate_proj/up_proj	128	6e-4	~28%
q/k/v_proj	48	4e-4	~30-47%

Delta-energy-weighted: 99.1% captured, reconstruction error 0.087.

lora_alpha == r == 16, so the PEFT/GGUF scale is 1.0 (exact delta). Use --lora-scaled 0.5 (llama.cpp) or a merge ratio to weaken the effect.

Usage

PEFT (transformers)

⚠️ Must load with AutoModelForImageTextToText (→ Qwen3_5ForConditionalGeneration). AutoModelForCausalLM resolves to the text-only Qwen3_5ForCausalLM whose parameter names (model.layers.*) do not match this adapter (model.language_model.layers.*).

import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer
from peft import PeftModel

model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3.6-27B", device_map="auto", dtype=torch.bfloat16)
model = PeftModel.from_pretrained(model, "hotdogs/thinkingcap-qwen36-r16-lora")

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.6-27B")
prompt = "Explain the theory of relativity"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0], skip_special_tokens=True))

llama.cpp (GGUF)

llama-cli -m Qwen3.6-27B.Q6_K.gguf \
    --lora thinkingcap_qwen36_r16.gguf \
    --lora-scaled 0.5 \
    -p "Explain the theory of relativity"

Requires a base GGUF of the same architecture (Qwen3.6-27B).

Files

• adapter_config.json — PEFT config (r=16, alpha=16, target modules)
• adapter_model.safetensors — LoRA weights (BF16, 159.5 MB)
• thinkingcap_qwen36_r16.gguf — llama.cpp LoRA (159.4 MB)
• tokenizer.* — tokenizer files copied from the base model

Notes

• Extracted with weight-diff-extraction (randomized SVD, CPU, ~11 min for 256 tensors).
• The delta includes both the ThinkingCap fine-tune and the abliteration applied by huihui-ai, relative to the Qwen3.6-27B base.
• Source model license: Qwen (Apache-2.0) + huihui-ai abliterated variant.