---

license: apache-2.0

tags:

- transformers

- gguf

- qwen3

- thinkingcap

- efficient-thinking

- reasoning

- token-efficient

- lora

- sft

- abliterated

- multi-token-prediction

- mtp

- llama.cpp

- text-generation

base_model:

- hotdogs/Qwen3.8-27B-thinkingcap-abliterated-preview

model:

- hotdogs/Qwen3.8-27B-thinkingcap-abliterated-preview-mtp-GGUF

datasets:

- hotdogs/thinkingcap-sft-qwen38-27b

pretty_name: Qwen3.8-27B ThinkingCap Abliterated (MTP GGUF)

---

Qwen3.8-27B ThinkingCap Abliterated (MTP GGUF)

llama.cpp GGUF from the ThinkingCap SFT-merged model

(hotdogs/Qwen3.8-27B-thinkingcap-abliterated-preview).

Trained from the on-policy oracle-verified dataset.

All 3 quantizations preserve the MTP layer (blk.64, 15 tensors

including 4 nextn.* projection tensors). 866 tensors total, 27.3B params.

Files

| File | Size | BPW | Notes |

|------|------|-----|-------|

| Qwen3.8-27B-thinkingcap-abliterated-preview-mtp-f16.gguf | 51 GB | 16.0 | lossless |

| Qwen3.8-27B-thinkingcap-abliterated-preview-mtp-Q6_K.gguf | 21 GB | 6.56 | best quality/size |

| Qwen3.8-27B-thinkingcap-abliterated-preview-mtp-Q4_K_M.gguf | 16 GB | ~5.1 | fastest |

MTP verification

import gguf
g = gguf.GGUFReader("Qwen3.8-27B-thinkingcap-abliterated-preview-mtp-f16.gguf")
mtp = [t.name for t in g.tensors if 'blk.64' in t.name]
print(len(mtp))  # 15

Serve (llama.cpp)

CUDA_VISIBLE_DEVICES=0,1,2,3,4 llama-server \
  -m Qwen3.8-27B-thinkingcap-abliterated-preview-mtp-Q6_K.gguf \
  --n-gpu-layers 999 --ctx-size 8192 --parallel 2 \
  --batch-size 4096 --flash-attn on \
  --temp 1 --top-k 20 --top-p 0.95 --min-p 0.0 --jinja

Smoke test

| Problem | Think chars | Answer | Correct |

|---------|-------------|--------|---------|

| 27 x 43 | 163 | 1161 | Yes |

| bat + ball = 1.10, bat = ball + 1.00 | 161 | ball = 0.05 | Yes |

| snail 10m wall, +3m day, -2m night | 359 | day 5 | Yes |

Base model (pre-SFT) used 4000+ think-chars on the snail problem.

ThinkingCap SFT brings it to 359 (-91%).