base_model: Qwen/Qwen3.8-27B
base_model_relation: finetune
library_name: transformers
tags:

• qwen3_8

• token-efficient

• efficient-thinking

• heretic

• uncensored

• decensored

• abliterated
  license: other
  license_name: polyform-small-business-1.0.0
  license_link: LICENSE
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This is a decensored version of bottlecapai/ThinkingCap-Qwen3.8-27B, made using Heretic v2.0.0.dev0+custom

Abliteration parameters

Performance

⚠️ Important Notice

This model has undergone substantial reduction of its safety alignment. As a result, it is more likely than standard models to generate harmful, inaccurate, biased, offensive, or otherwise inappropriate content.

Intended Use

For research and experimentation only, including safety research, alignment studies, and red-teaming. Please avoid deploying it in public or end-user-facing services.

User Responsibility

All outputs should be treated as untrusted and independently verified before use. Users are solely responsible for:

• Evaluating the accuracy and suitability of generated content
• Implementing appropriate safeguards and human oversight
• Complying with applicable laws, regulations, licenses, and ethical standards

Use of this model is entirely at your own risk.

Disclaimer

OS-Software provides this model without warranties of any kind and assumes no liability for any direct or indirect damages, losses, misuse, or legal consequences arising from its use.

Acknowledgements

Thanks to the base model developers, p-e-w for Heretic, and the wider open-source community.

This is a derivative work released under the base model’s applicable license. All rights to the base model remain with their respective owners.

ThinkingCap: Qwen 3.8 27B

In the second installment of our ThinkingCap series, we focus on maintaining the performance of
Qwen3.8-27B (Qwen Team, 2026) on challenging and agentic
tasks while delivering a substantial reduction in thinking verbosity. ThinkingCap Qwen3.8-27B cuts
reasoning tokens by 37% on average (11% to 66% depending on the benchmark)
and holds an average accuracy of 85.8% against the base model's 86.6%.
It shines in long-context retrieval, where it cuts reasoning by 39% with accuracy
intact (+2.3pp). Check our blogpost for more details.

Token efficiency and benchmark performance

Thinking mode comparison

We recommend using this model at the xhigh thinking effort for the best balance between accuracy and reasoning token usage. At lower efforts, the ThinkingCap treatment amplifies the effect of the effort setting while keeping its original trade-off. We plan to focus on improving the individual thinking modes in a future release.

Usage

HuggingFace Transformers

from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained("bottlecapai/ThinkingCap-Qwen3.8-27B", dtype="bfloat16")
proc = AutoProcessor.from_pretrained("bottlecapai/ThinkingCap-Qwen3.8-27B")

Check https://huggingface.co/Qwen/Qwen3.8-27B for recommended usage, sampling params etc.

vLLM / SGLang

Serve the bf16 model with either engine. The reasoning parser returns the thinking in a separate reasoning / reasoning_content field instead of inline in content before </think>, and the tool-call parser turns the model's XML tool calls into structured tool_calls — the same flags the base model's serving recipes use. The model's own MTP (multi-token-prediction / NextN) head gives self-speculative decoding with no separate draft model:

# vLLM — standard
vllm serve bottlecapai/ThinkingCap-Qwen3.8-27B \
  --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml
# vLLM — with MTP self-speculative decoding
vllm serve bottlecapai/ThinkingCap-Qwen3.8-27B \
  --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

# SGLang — standard
python -m sglang.launch_server --model-path bottlecapai/ThinkingCap-Qwen3.8-27B --trust-remote-code \
  --reasoning-parser qwen3 --tool-call-parser qwen3_coder
# SGLang — with MTP self-speculative decoding
python -m sglang.launch_server --model-path bottlecapai/ThinkingCap-Qwen3.8-27B --trust-remote-code \
  --reasoning-parser qwen3 --tool-call-parser qwen3_coder \
  --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. On these bf16 weights, vLLM 0.29.0 with num_speculative_tokens=3 accepted 53% of drafted tokens across our xhigh evaluation runs — about 2.6 tokens per decoding step, identical to the base model's 54% and 2.6 — ranging from 2.5 on LiveCodeBench to 2.9 on τ²-bench and AA-LCR; the shorter traces at medium/low lift this to 3.2–3.3.

Either server speaks the OpenAI Chat Completions API. One request covers text, images and the thinking-effort knob:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="-")   # SGLang: port 30000
r = client.chat.completions.create(
    model="bottlecapai/ThinkingCap-Qwen3.8-27B",
    messages=[{"role": "user", "content": [
        {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}},
        {"type": "text", "text": "What is happening in this picture?"},
    ]}],
    temperature=1.0, top_p=0.95,
    extra_body={"top_k": 20,
                "chat_template_kwargs": {"reasoning_effort": "xhigh"}},   # xhigh (default) | medium | low
)
print(r.choices[0].message.reasoning)               # the thinking (`reasoning_content` on SGLang)
print(r.choices[0].message.content)                 # the answer

A text-only request is the same call with a plain string as content.

Quantized versions

Same checkpoint, chat template and license, quantized for smaller footprints and faster serving:

• FP8 — bottlecapai/ThinkingCap-Qwen3.8-27B-FP8 — FP8 block-wise, vLLM, 31 GB; Hopper and Blackwell
• GGUF — bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF — IQ4_XS to f16 (16–55 GB), llama.cpp / LM Studio / Ollama; CUDA, Apple Metal, Vulkan or CPU
• NVFP4 — bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4 — NVFP4 weight-only, vLLM, 21 GB; Hopper (Marlin kernel) and Blackwell
• NVFP4 W4A4 — bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ — NVFP4 weights and activations (AWQ), vLLM, 23 GB; Blackwell only

All builds are gathered in this collection.

Where to find us

Need even more efficiency? The open release is production-ready. Our enterprise versions go further — fewer thinking tokens still, tuned to your workload, at matched accuracy on your own tasks. Built for AI labs, inference providers and enterprises running models at scale. Deployed on your infrastructure, or in the cloud and region you choose.
Talk to our team

License

ThinkingCap: PolyForm Small Business 1.0.0 + BottleCap personal-use grant (see LICENSE).

Upstream Qwen materials: Apache-2.0 (see NOTICE).

Commercial license: contact BottleCap AI.

Citation

If you use this model, please cite:

@misc{ThinkingCap-Qwen3.8-27B,
  title     = {bottlecapai/ThinkingCap-Qwen3.8-27B},
  author    = {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 Kubista, Daniel and Krus, Frantisek and Mikolov, Tomas},
  year      = {2026},
}