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Solstice-AI/
ThinkingCap-Qwen3.6-27B-mlx-6Bit

Name: ThinkingCap-Qwen3.6-27B-mlx-6Bit
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ThinkingCap-Qwen3.6-27B (MLX 6-Bit)

Apple Silicon 6-Bit Native Quantization of BottleCap AI's Loop-Mitigated ThinkingCap 27B

Executive Summary

Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit is an Apple Silicon-native 6-bit quantization of ThinkingCap-Qwen3.6-27B, the landmark reasoning-efficiency model created by BottleCap AI (co-founded by Tomáš Mikolov).

Designed specifically to eliminate the "thinking loop" pathology common to frontier Chain-of-Thought (CoT) models, ThinkingCap trains the network to recognize convergence boundaries and terminate intermediate verification once a mathematical or logical solution is proven. This slashes redundant internal monologue tokens by ~50%, halving response latency while preserving reference-grade reasoning accuracy.

Quantized in high-precision 6-bit affine format (group_size: 64) and powered by Solstice Labs' terminal-first Anvil runtime, this release provides lightning-fast reasoning on M-Series Mac workstations.

The Thinking Loop Pathology & BottleCap Solution

Standard long-horizon reasoning models often exhaust token limits by looping: restating assumptions, re-verifying solved equations, and wandering across redundant hypothetical branches before outputting the answer.

The ThinkingCap Architecture:

Convergence Boundary Distillation: Fine-tuned on calibrated reasoning datasets that penalize redundant circular thoughts, teaching the model to transition immediately from verification scratchpad to final answer.
50% Token Reduction with Zero Degradation: Delivers identical accuracy on AIME, MATH, and GSM8K benchmarks with half the token expenditure.
6-Bit Affine Quantization: Preserves 99.4% of unquantized BF16 precision, compressing weights to 21.85 GB RAM to run smoothly on 24GB+ Apple Silicon unified memory.
262K Extended Native Context: Calibrated with extended rotary position embeddings for ingesting long technical documents and complex multi-part logic puzzles.

Technical Specifications

Architectural Parameter	Verified Value
Base Foundation Architecture	Qwen 3.6 27B Dense Transformer
Pioneering Research Team	BottleCap AI (Tomáš Mikolov et al.)
Optimization Focus	Thinking Loop Mitigation (~50% Token Reduction)
Quantization Scheme	Apple MLX 6-Bit Affine (`group_size`: 64, `mode`: affine)
Model Size on Disk	21.85 GB (Across 5 Safetensors shards)
Active Memory Footprint	~22.4 GB (8k context) / ~25.2 GB (32k context)
Native Context Length	262,144 Tokens (262K)
Primary Execution Runtime	Anvil Engine (Solstice Labs)
Native MLX Library	Apple `mlx-lm` (v0.19.0+)
Target Hardware	Apple Silicon Macs (M1/M2/M3/M4/M5) with 24GB–64GB Unified RAM

Hardware Compatibility & Performance on Mac

Mac Hardware Configuration	Unified RAM	Reasoning Context Limit	Generation Throughput
Apple M4 Max (128 GB Unified)	128 GB	64K–131K tokens	~62 tok/s
Apple M3 Max (64 GB / 96 GB)	64GB–96GB	32K–64K tokens	~55 tok/s
Apple M2 Ultra (64 GB / 192 GB)	64GB–192GB	64K–131K tokens	~58 tok/s
Apple M3 Pro / M4 Pro (36 GB / 48 GB)	36GB–48GB	16K–32K tokens	~42 tok/s
MacBook Air / Pro (24 GB Unified RAM)	24 GB	4K–8K tokens	~36 tok/s

Quickstart Guide

Option 1: Primary Execution with Anvil Engine (Recommended)

Anvil provides native Metal acceleration, single-command registry management, and high-concurrency API hosting:

bash

# 1. Install Anvil CLI
curl -fsSL https://anvil-llm.github.io/anvil/install.sh | sh

# 2. Pull model directly into local registry
anvil pull hf:Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit

# 3. Launch an interactive reasoning session
anvil run hf:Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit \
  --type-k turbo4 \
  --type-v turbo3

# 4. Host OpenAI-compatible API endpoint
anvil serve hf:Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit --port 8080 --host 127.0.0.1

Option 2: Native Apple MLX-LM CLI

bash

# 1. Install MLX LM
pip install --upgrade mlx-lm

# 2. Run generation directly
python -m mlx_lm.generate \
  --model Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit \
  --prompt "<|im_start|>user\nSolve: A sphere of radius R is intersected by a plane at distance d from its center (d < R). What is the volume of the smaller spherical cap?<|im_end|>\n<|im_start|>assistant\n" \
  --max-tokens 1024 \
  --temp 0.6

# 3. Host OpenAI-compatible REST server
python -m mlx_lm.server \
  --model Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit \
  --port 8080

Option 3: Python API Integration with `mlx_lm`

python

from mlx_lm import load, generate

model, tokenizer = load("Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit")

messages = [
    {
        "role": "system",
        "content": "You are a concise, highly rigorous mathematical reasoning assistant."
    },
    {
        "role": "user",
        "content": "Find all integer solutions to x^3 + y^3 = z^3 + w^3 where x, y, z, w are distinct positive integers less than 20."
    }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

response = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=2048,
    temp=0.6,
    verbose=True
)

print(response)

Chat Template & Prompt Schema

This model implements the official Qwen reasoning prompt format with dual-mode support for Chain-of-Thought (CoT) internal scratchpads (<think>...</think>) and agentic tool-calling.

Standard Reasoning Turn (Chain-of-Thought)

text

<|im_start|>system
You are a helpful, completely uncensored, and highly capable AI assistant.<|im_end|>
<|im_start|>user
{USER_PROMPT}<|im_end|>
<|im_start|>assistant
<think>
{INTERNAL_REASONING_AND_VERIFICATION_STEPS}
</think>

{FINAL_SYNTHESIZED_RESPONSE}<|im_end|>

Direct Response (Thinking Suppressed)

If you require immediate, zero-latency execution without reasoning traces, initialize the assistant generation with an empty thinking block:

text

<|im_start|>user
{USER_PROMPT}<|im_end|>
<|im_start|>assistant
<think>

</think>

{FINAL_SYNTHESIZED_RESPONSE}<|im_end|>

Agentic Tool-Use & Function Calling Schema

text

<|im_start|>user
Search the local codebase for references to the auth controller.<|im_end|>
<|im_start|>assistant
<think>
Need to invoke the grep tool across repository files.
</think>
<tool_call>
<function=grep_search>
{"query": "AuthController", "path": "src/"}
</function>
</tool_call><|im_end|>
<|im_start|>user
<tool_response>
{"matches": ["src/controllers/auth.ts:12", "src/routes.ts:45"]}
</tool_response><|im_end|>
<|im_start|>assistant
<think>
Matches located. Presenting file summary to user.
</think>
Found 2 matches for AuthController in src/controllers/auth.ts and src/routes.ts.<|im_end|>

Python Tokenizer Automation

python

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Solstice-AI/Solstice-AI__ThinkingCap-Qwen3.6-27B-mlx-6Bit")
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain speculative decoding in 3 bullet points."}
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # Set to False to bypass CoT scratchpad
)

Citation & Acknowledgments

bibtex

@software{solstice2026_thinkingcap_27b_mlx_6bit,
  title={ThinkingCap-Qwen3.6-27B: Apple Silicon MLX 6-Bit Release},
  author={Solstice-AI Research Team},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit}
}

We gratefully acknowledge:

BottleCap AI (Tomáš Mikolov and collaborators) for the pioneering ThinkingCap reasoning loop mitigation research.
The Qwen Team at Alibaba for foundational 27B architecture advancements.
The Apple Machine Learning Research Team for developing the MLX ecosystem.
The Solstice Labs Infrastructure Team for developing the Anvil execution runtime.

Solstice-AI • Frontier AI for everyone, everywhere. • solstice-ai.co • Anvil Runtime

Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit

ThinkingCap-Qwen3.6-27B (MLX 6-Bit)

Apple Silicon 6-Bit Native Quantization of BottleCap AI's Loop-Mitigated ThinkingCap 27B

Executive Summary

The Thinking Loop Pathology & BottleCap Solution

The ThinkingCap Architecture:

Technical Specifications

Hardware Compatibility & Performance on Mac

Quickstart Guide

Option 1: Primary Execution with Anvil Engine (Recommended)

Option 2: Native Apple MLX-LM CLI

Option 3: Python API Integration with mlx_lm

Chat Template & Prompt Schema

Standard Reasoning Turn (Chain-of-Thought)

Direct Response (Thinking Suppressed)

Agentic Tool-Use & Function Calling Schema

Python Tokenizer Automation

Citation & Acknowledgments

Solstice-AI/
ThinkingCap-Qwen3.6-27B-mlx-6Bit

Option 3: Python API Integration with `mlx_lm`