A rank-16 LoRA adapter that converts Claude 4.7 Opus reasoning style into Kimi K2.6 reasoning style — on the same 35B Mixture-of-Experts base model.

No training. Pure linear algebra.

🔬 How It Works: Weight-Diff SVD Extraction

Both lordx64 models share the exact same base (Qwen/Qwen3.6-35B-A3B) and were fine-tuned with LoRA (merged back).

Mathematically:

W_opus  = W_base + delta_Opus
W_kimi  = W_base + delta_Kimi

delta(Opus_to_Kimi) = W_kimi - W_opus
                    = (W_base + delta_Kimi) - (W_base + delta_Opus)
                    = delta_Kimi - delta_Opus

The base model cancels out — only the reasoning delta remains!

SVD Compression

The raw delta is 70+ GB. We compress it to rank-16 LoRA via truncated SVD:

python

for each attention weight tensor:
    delta = W_kimi - W_opus                  # [out, in]
    U, S, Vh = SVD(delta)                   # decompose
    lora_B = U[:, :16] * sqrt(S[:16])       # [out, 16]
    lora_A = sqrt(S[:16]) * Vh[:16, :]      # [16, in]

Input: 2x 72 GB models (~145 GB disk)
VRAM used: ~3 GB (tensor-by-tensor, no GPU needed)
Compute: ~44 SVDs on CPU (< 3 minutes)
Output: 7.2 MB LoRA adapter (rank=16, attention-only)

Target Modules

Only full-attention layers (every 4th layer in Qwen3.5-MoE):

Layer	q_proj	k_proj	v_proj	o_proj
3, 7, 11, 15, 19, 23, 27, 31	✅	✅	✅	✅
35, 39	del=0	del=0	del=0	del=0

⚡ Interesting finding: Layers 35 and 39 have zero delta — the Kimi fine-tune did not touch these layers at all!

Why Attention-Only?

The existing Claude Opus LoRA adapter (13.8 MB, r=16) is attention-only (q/k/v/o_proj). We match the same target modules for compatibility.

The 3D expert tensors (256, 2048, 512) were intentionally skipped — both for compatibility with the existing adapter and because reasoning style is primarily encoded in attention patterns, not expert FFN weights.

🌍 METHOD — ภาษา / Languages / 语言

คู่มือเทคนิค Weight-Diff SVD Extraction มีให้อ่านใน 5 ภาษา: The universal extraction guide is available in 5 languages:

ไฟล์	ภาษา / Language
METHOD.md	🇹🇭 ไทย
METHOD_EN.md	🇬🇧 English
METHOD_ZH.md	🇨🇳 中文
METHOD_JP.md	🇯🇵 日本語
METHOD_VN.md	🇻🇳 Tiếng Việt

ทุกไฟล์มีเนื้อหาครบถ้วน: เงื่อนไขการใช้งาน, 5 ขั้นตอน, คณิตศาสตร์, ตัวอย่างโมเดลอื่น, troubleshooting, และการอ้างอิง All files contain: requirements, 5 steps, math, examples for other models, troubleshooting, and references.

📦 Available Formats

PEFT (Python)

python

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained(
    "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, "hotdogs/qwen3.6-35b-opus-to-kimi-lora")
model = model.merge_and_unload()

GGUF (llama.cpp)

bash

./llama-cli \
    -m Qwen3.6-35B-A3B-Claude-Opus-Q6_K.gguf \
    --lora qwen3.6-35b-opus-to-kimi-lora.gguf \
    -p "Solve this math problem step by step..."

⚠️ Prerequisite: The Docker command below uses the Opus reasoning adapter from lordx64. Download it first:

bash
1wget https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled/resolve/main/adapter_model.safetensors 2# Or use the GGUF version for llama.cpp: 3# Convert with: python3 llama.cpp/convert_lora_to_gguf.py /path/to/opus-adapter

Or use only the Kimi adapter without Opus: --lora-scaled /models/qwen3.6-35b-opus-to-kimi-lora.gguf:1.0

llama.cpp Server (Docker) — การใช้งานแบบ Multi-LoRA Stacking 🔥

🌐 สแต็ก LoRA หลายตัวพร้อมกัน — รวมโมเดลพื้นฐานแบบ uncensored + Opus reasoning LoRA + Kimi style LoRA เข้าด้วยกันในเซิร์ฟเวอร์เดียวที่เข้ากันได้กับ OpenAI API:

llama.cpp Server (Docker) — Multi-LoRA Stacking 🔥

Combine the uncensored base model + Opus reasoning LoRA + Kimi style LoRA into one OpenAI-compatible API server:

bash

sudo docker run --rm -p 8080:8080 \
  -v /path/to/models/:/models \
  --gpus all \
  --env CUDA_VISIBLE_DEVICES=0,1,2,3 \
  ghcr.io/ggml-org/llama.cpp:server-cuda \
  -m /models/llmfan46_Qwen3.6-35B-A3B-uncensored-heretic-Q6_K.gguf \
  --lora-scaled /models/lordx64_Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-adapter-F16.gguf:0.6,/models/qwen3.6-35b-opus-to-kimi-lora.gguf:0.8 \
  --host 0.0.0.0 --port 8080 \
  --n-gpu-layers 999 \
  --tensor-split 4,13,12,12 \
  --ctx-size 131072 \
  --batch-size 4096 \
  --ubatch-size 512 \
  --cache-type-k q4_0 \
  --cache-type-v q4_0 \
  -fa on \
  --mlock \
  --jinja

What this does:

Component	Purpose	Weight
`llmfan46_...-heretic-Q6_K.gguf`	Uncensored base (35B MoE)	🏛️ Base
`lordx64_...-Opus-...-adapter-F16.gguf`	Claude Opus reasoning (concise)	0.6 = 60%
`qwen3.6-35b-opus-to-kimi-lora.gguf`	→ Kimi K2.6 style (verbose) 🔥	0.8 = 80%

Result: Uncensored base + Opus reasoning structure + Kimi verbose style — all in one model!

Key flags explained:

Flag	Purpose
`--lora-scaled A:α,B:β`	Stack multiple LoRA adapters with independent scales
`--n-gpu-layers 999`	Offload all layers to GPU
`--tensor-split 4,13,12,12`	Split across 4 GPUs (adjust for your setup)
`--ctx-size 131072`	128K context window
`--cache-type-k q4_0`	KV cache in 4-bit quantization (saves VRAM)
`--cache-type-v q4_0`	Value cache in 4-bit quantization
`-fa on`	Flash Attention enabled
`--mlock`	Lock model in RAM (prevents swap)
`--jinja`	Use Jinja2 chat templates
`--lora`	Apply LoRA adapter (applied first, before scaled)
`--lora-scaled`	Apply LoRA with scale (comma-separated for multiple)

🛡️ 3-Layer Stack with Refusal Removal LoRA

For the purest uncensored stack using weight-diff extracted LoRAs:

Layer	Component	Purpose
1	Opus GGUF (base model)	Qwen3.6-35B + Opus reasoning
2	refusal-removal-lora	🛡️ Remove refusals (uncensored)
3	opus-to-kimi-lora (scale 0.5)	🎨 Kimi K2.6 verbose style

bash

docker run --gpus all -p 8080:8080 \
  -v /path/to/models:/models \
  ghcr.io/ggml-org/llama.cpp:server-cuda \
  -m /models/lordx64_Qwen3.6-35B-A3B-Claude-4.7-Opus-Q6_K.gguf \
  --lora /models/qwen3.6-35b-refusal-removal-lora.gguf \
  --lora-scaled /models/qwen3.6-35b-opus-to-kimi-lora.gguf:0.5 \
  --host 0.0.0.0 --port 8080 \
  --n-gpu-layers 999 \
  --ctx-size 131072 \
  --batch-size 4096 \
  -fa on

🔬 Technical note: The refusal-removal LoRA was extracted via Weight-Diff SVD from huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated minus lordx64/...Opus. It modifies only o_proj in 10 layers (3,7,11,15,19,23,27,31,35,39) — an extremely sparse signal compared to full distillation (Kimi LoRA touches all 44 attention tensors).

Old stack (uncensored GGUF base):

Single GPU alternative:

bash

sudo docker run --rm -p 8080:8080 \
  -v /path/to/models/:/models \
  --gpus all \
  ghcr.io/ggml-org/llama.cpp:server-cuda \
  -m /models/llmfan46_Qwen3.6-35B-A3B-uncensored-heretic-Q6_K.gguf \
  --lora-scaled /models/lordx64_Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-adapter-F16.gguf:0.6,/models/qwen3.6-35b-opus-to-kimi-lora.gguf:0.8 \
  --host 0.0.0.0 --port 8080 \
  --n-gpu-layers 999 \
  --ctx-size 32768 \
  --batch-size 2048 \
  --cache-type-k q4_0 --cache-type-v q4_0 \
  -fa on --mlock --jinja

API Usage (OpenAI-compatible):

bash

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "Explain quantum entanglement step by step"}
    ],
    "temperature": 0.7,
    "max_tokens": 4096
  }'

💡 Tip: Adjust LoRA scales to fine-tune the reasoning style:

0.6:0.8 — Balanced (Opus structure + Kimi verbosity)

0.3:1.0 — Heavy Kimi style

1.0:0.2 — Mostly Opus, slight Kimi touch

0.0:1.0 — Pure Kimi style (skip Opus adapter entirely)

📊 Comparison: Opus vs Kimi Reasoning

Trait	Claude Opus	+ Kimi LoRA
Thinking tokens (mean)	849	2,933 (3.5x longer)
Thinking tokens (p95)	2,404	9,764
Style	Concise, direct	Verbose, deliberate
Best for	Quick reasoning	Deep multi-step reasoning

🛠️ Technical Details

Parameter	Value
Method	Weight-diff SVD extraction
Rank	16
LoRA Alpha	16
Target modules	q_proj, k_proj, v_proj, o_proj
Tensors extracted	44 (attention weights across 11 layers)
Tensor shapes	q:[8192,2048] k/v:[512,2048] o:[2048,4096]
Adapter size	7.2 MB (PEFT) / 14 MB (GGUF F32)
Precision	BF16 to F32 (GGUF)
Extraction time	~3 min (CPU SVD)
Disk needed	~145 GB (temporary, for both full models)
VRAM needed	~3 GB (no GPU required)

🧪 Reproduction

Full extraction script and methodology available in the UKA Hermes Agent session log.

bash

# Quick reproduction
python3 extract_lora_diff.py \
    --opus-path ./model_opus \
    --kimi-path ./model_kimi \
    --rank 16 \
    --output ./opus-to-kimi-lora

👩‍💻 Credits

UKA (Hermes Agent) — designed the weight-diff SVD technique, wrote all extraction code, authored this README
lordx64 — trained the source models (Opus, Kimi)
Qwen Team — base model Qwen3.6-35B-A3B
Bas95 — original reasoning distillation datasets
Hermes Agent — nousresearch/hermes-agent

📄 License

Apache 2.0 — same as the source models.