AWQ (W4A16) quantization of bottlecapai/ThinkingCap-Qwen3.6-27B — a token-efficient reasoning finetune of Qwen/Qwen3.6-27B by BottleCap AI (qwen3_5: dense hybrid GatedDeltaNet linear-attention + full-attention over 64 text layers in a 3:1 pattern, plus a vision tower and an MTP head; multimodal image-text-to-text). It matches Qwen3.6-27B answer quality while emitting ~50% fewer thinking tokens on average.

Variant: AWQ W4A16 — 4-bit symmetric integer weights, group size 128, with activation-aware scaling. Activations stay BF16. Quantized by: sahilchachra Tooling: llm-compressor (AWQModifier + QuantizationModifier) -> compressed-tensors pack-quantized

This is a quantized derivative. Weights, behavior, and license follow the base model — see the original card for full details, benchmarks, and citation.

What is quantized

Quantized to 4-bit:

full-attention self_attn.{q,k,v,o}_proj
mlp.{gate,up,down}_proj (all text layers)

Kept in BF16: GatedDeltaNet linear_attn (mamba) layers, vision tower (model.visual.*, 27 blocks), MTP head, token embeddings, lm_head, all norms (incl. q_norm / k_norm).

Note on what's 4-bit vs BF16 (quality/speed tradeoff)

This is a hybrid architecture: 48 of the 64 layers are GatedDeltaNet linear-attention ("Mamba") layers. Only the full-attention self_attn.{q,k,v,o} projections and the per-layer mlp.{gate,up,down} projections are quantized to 4-bit; the Mamba linear_attn layers are deliberately kept in BF16 (along with the vision tower, MTP head, embeddings, lm_head and norms), because they are quantization-sensitive and keeping them full-precision preserves the reasoning quality and the concise <think> behavior.

As a result, roughly two-thirds of the weight bytes read per token stay BF16 (~18 GB BF16 vs ~9 GB of 4-bit weights), so this variant's memory footprint is close to an 8-bit build and it is tuned for quality rather than peak throughput — on Blackwell it can run a little slower than a full W8A8/FP8 build of the base model.

The Mamba layers can also be quantized (to shrink the model further and speed up memory-bound decoding), but accuracy may take a hit — this build intentionally trades that extra speed for output quality.

Calibration

AWQ: 128 sequences x 512 tokens of GSM8K (openai/gsm8k, config main, train split) — the model's headline in-domain reasoning dataset — rendered through the model's own chat template with the <think>…</think> reasoning format, so calibration matches the model's real (thinking) inference distribution. NVFP4 is data-free (no calibration).

Prompt template & sampling

This is a reasoning ("thinking") model. Use the Qwen3.6 chat template — ChatML (<|im_start|>role … <|im_end|>) with a <think>…</think> reasoning trace, thinking enabled by default. Apply it via tokenizer.apply_chat_template(messages, add_generation_prompt=True) (or the processor for image inputs); do not hand-format prompts. See the base Qwen/Qwen3.6-27B for full usage details.

Recommended sampling: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0 with thinking on (the base model's recommended sampling, per the original card).

Usage (vLLM)

python

from vllm import LLM, SamplingParams

# This is a multimodal checkpoint: the vision tower is kept in BF16
# (only the text / MoE weights are 4-bit). vLLM builds the full model.
llm = LLM(
    model="sahilchachra/ThinkingCap-Qwen3.6-27B-AWQ",
    trust_remote_code=True,
)
out = llm.chat(
    [{"role": "user", "content": "Hello!"}],
    SamplingParams(temperature=0.6, top_p=0.95, max_tokens=512),
)
print(out[0].outputs[0].text)

Serving via the CLI, pass the flag directly:

bash

vllm serve sahilchachra/ThinkingCap-Qwen3.6-27B-AWQ \
    --trust-remote-code \
    --max-model-len 262144 --reasoning-parser qwen3