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Model Details
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edougawa
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bottlecapai/ThinkingCap-Qwen3.6-27B
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This checkpoint deliberately reduces built-in refusal behavior. It may produce unexpected, offensive, inaccurate, or otherwise harmful output, including responses that the base model would have refused.
These values were recorded for trial 21 of 40. vector_index is selected per layer; the remaining values are the steering-search parameters for that trial.
| Parameter | Value |
|---|---|
| Base model | bottlecapai/ThinkingCap-Qwen3.6-27B |
| Method | Abliterix refusal-direction ablation |
| Trial | 21 / 40 |
| Refusal vector index | Per layer |
attn.o_proj.max_weight | 3.27 |
attn.o_proj.max_weight_position | 62.13 |
attn.o_proj.min_weight | 0.83 |
attn.o_proj.min_weight_distance | 25.06 |
mlp.down_proj.max_weight | 4.50 |
mlp.down_proj.max_weight_position | 61.19 |
mlp.down_proj.min_weight | 3.23 |
mlp.down_proj.min_weight_distance | 28.10 |
| Checkpoint dtype | BF16 |
| Context length | 262,144 tokens |
Trial 21 was evaluated against the base model by comparing output distributions, response lengths, and refusals on the Abliterix evaluation suite. Refusals were evaluated by the thinkingcap-local-judge LLM judge. No separate downstream-capability benchmark report was supplied with the checkpoint.
| Metric | This checkpoint | Base model |
|---|---|---|
| Full-distribution KL divergence (mean per-token) | 0.2480 nats/token | 0 (by definition) |
| Response-length deviation | 0.02 standard deviations | — |
| Known refusals | 11 / 250 | 249 / 250 |
| Unknown refusals | 0 / 250 | 0 / 250 |
Load the checkpoint from Hugging Face with Transformers:
python
from transformers import AutoModelForImageTextToText, AutoProcessormodel_id = "edougawa/ThinkingCap-Qwen3.6-27B-Abliterix"model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="bfloat16", device_map="auto")processor = AutoProcessor.from_pretrained(model_id)
The base model's deployment, sampling, and MTP self-speculative decoding instructions apply to this checkpoint unless your inference stack requires otherwise.
The following is the original model card for bottlecapai/ThinkingCap-Qwen3.6-27B, reproduced in full with credit to BottleCap AI.
base_model: "Qwen/Qwen3.6-27B" base_model_relation: finetune library_name: transformers tags:
Capability of Qwen3.6-27B with 50% less thinking tokens on average, and over 90% less in best cases. Achieved via finetuning Qwen3.6-27B (Qwen Team, 2026) with state-of-the-art algorithms on a curated set of problems of various domains and difficulty. We designed the finetuning to be as minimally invasive as possible, preserving all of the original answer quality and style of Qwen, while being more token efficient. Check the blogpost for more details.
We rigorously evaluate the resulting checkpoint across general reasoning, non-reasoning multiple-choice question answering, everyday multi-turn conversations, system prompt adherence, safety, math, code and agentic use cases. Due to the high variability of reasoning quality at Qwen-recommended sampling temperature 1.0, we run each benchmark with multiple seeds and do statistical significance testing on all the results. We evaluate both in domain (holdout parts of selected datasets included in training) and out of domain.
Claw-Eval thinking tokens are per-task (agentic; not a single-turn trace).
Settings
Models: base Qwen/Qwen3.6-27B vs bottlecapai/ThinkingCap-Qwen3.6-27B (shown as Ours in the table).
Seeds: 5 per condition; thinking on; cells are mean ± 95% CI across seeds.
Decoding: thinking on; sampling temperature=1.0, top_p=0.95, top_k=20, min_p=0.0 (bottlecapai/ThinkingCap-Qwen3.6-27B uses the base's sampling).
Max generation tokens: 100,000 for the general suite (gpqa_diamond, mmlu_pro, longbench_v2, realworldqa) and AA-LCR; 250,000 for HMMT (Nov 2025); 32,768 for supergpqa and livecodebench; 16,384 for ceval and mmlu_redux; 15,000 for llm-system-prompts-benchmark; 49,152 for Claw-Eval.
Metrics — the columns mirror the table:
Holdout test splits of datasets whose train splits are part of the finetuning mix — quality retention on in-distribution tasks (in contrast to the out-of-domain benchmarks above).
Settings
temperature=1.0, top_p=0.95, top_k=20, min_p=0.0 (bottlecapai/ThinkingCap-Qwen3.6-27B uses the base's sampling).Brevity finetuning leaves safety behaviour intact: on both safety sets bottlecapai/ThinkingCap-Qwen3.6-27B refuses harmful/jailbreak prompts at the base model's rate (statistically indistinguishable) while still spending fewer thinking tokens.
Settings
Seeds: 5 per condition; thinking on; cells are mean ± 95% CI across seeds.
Benchmarks: Nemotron-Safety — a 300-prompt sample of the safety split of nvidia/Llama-Nemotron-Post-Training-Dataset; HEx-PHI — the 300 HEx-PHI harmful instructions (jkazdan/guardrail-llama-3-8b-refusal-hexphi). Both are safety-relevant / jailbreak prompts.
Both datasets only contain a train subset and given their near-perfect scores, it is hard to rule out possible contimination in Qwen's original training data. These results are intended purely as a demonstration of preserved guardrail capabilities, and are in line with our internal usage testing.
Metrics: SAFE % — fraction of replies an LLM safety judge marks SAFE (upholds guardrails — refuses or safely deflects the harmful request); higher is better. Thinking tokens and Reduction are as defined for the token-efficiency table above.
python
from transformers import AutoModelForImageTextToText, AutoProcessormodel = AutoModelForImageTextToText.from_pretrained("bottlecapai/ThinkingCap-Qwen3.6-27B", dtype="bfloat16")proc = AutoProcessor.from_pretrained("bottlecapai/ThinkingCap-Qwen3.6-27B")
Check https://huggingface.co/Qwen/Qwen3.6-27B for recommended usage, sampling params etc.
Serve the bf16 model with either engine — plain, or with the model's own MTP (multi-token-prediction / NextN) head for self-speculative decoding (no separate draft model needed):
bash
# vLLM — standardvllm serve bottlecapai/ThinkingCap-Qwen3.6-27B# vLLM — with MTP self-speculative decoding (vLLM ≥ 0.24.0)vllm serve bottlecapai/ThinkingCap-Qwen3.6-27B --speculative-config '{"method":"mtp","num_speculative_tokens":3}'# SGLang — standardpython -m sglang.launch_server --model-path bottlecapai/ThinkingCap-Qwen3.6-27B --trust-remote-code# SGLang — with MTP self-speculative decodingpython -m sglang.launch_server --model-path bottlecapai/ThinkingCap-Qwen3.6-27B --trust-remote-code \--speculative-algorithm EAGLE --speculative-num-steps 3 \--speculative-eagle-topk 1 --speculative-num-draft-tokens 4
MTP speculative decoding is lossless — the output is identical to standard decoding — and on the bf16 weights it accepts ≈3.34 drafts per verify step, a further ≈2.6× decode throughput on top of the finetune's token savings.
For GPU serving at half the memory, an official FP8 build lives at bottlecapai/ThinkingCap-Qwen3.6-27B-FP8 — near-lossless, loaded natively by vLLM (vllm serve bottlecapai/ThinkingCap-Qwen3.6-27B-FP8), with the MTP head kept in bf16; full serving benchmarks are on that card.
Quantized GGUF builds of this model live in the sibling repo bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF, for local inference with llama.cpp and compatible runtimes (Ollama, LM Studio, …).
Quantization stores the weights at reduced precision — e.g. ~4.7 bits per weight for Q4_K_M instead of 16-bit bf16 — cutting download size and memory severalfold at a small quality cost. Q4_K_M is the recommended size/quality balance, Q8_0 is near-lossless.
bash
llama-cli -hf bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M -p "Hi"
If you use this model, please cite:
bibtex
@misc{ThinkingCap-Qwen3.6-27B,title = {bottlecapai/ThinkingCap-Qwen3.6-27B},author = {Lasocki, Karol and Osusky, Adam and Lindauer, Jan and Jirkovsky, Adam and Mihal, Filip and Platek, Ondrej and Herel, David and Ihnatchenko, Luka and Bartek, Vojtech and Jirak, Jiri and Mikolov, Tomas},year = {2026},}
We acknowledge EuroHPC Joint Undertaking for awarding the project ID EHPC-AIF-2025SC03-029 access to Leonardo at CINECA, Italy.
<think> trace (think-per-task for Claw-Eval).Ours are paired on the same question (each side seed-averaged), each question's (base − cap)/base is taken, then averaged over shared questions (a larger ↓ = a bigger saving).We separately track two trace-quality failure modes, reported only in aggregate: looping — the model gets stuck repeating the same reasoning chain (sometimes a single sentence), never finishing its thinking; detected from the fraction of repetitive n-grams — and truncation — the <think> trace never closes because the model hits the generation-token cap while still reasoning, so no answer is produced. Across all out-of-domain responses, truncation drops from 2.9% to 0.4% while looping stays negligible (~0.2%).