Qwen3.8-27B-TWIN-TURBO-Cold-Fusion-709-L (oQ4e Apple Silicon MLX)

Official Solstice-AI Apple Silicon Release • 4-Bit Mixed Precision • 262K Tokens (262,144) Context Window • Native Hardware MTP Speculation • Solstice 10-Level Cognitive System

Original Architecture by Qwen / Alibaba • Twin-Turbo Fine-Tune by DavidAU • oQ4e Mixed-Precision by Solstice-AI

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Executive Overview

Solstice-AI/Qwen3.8-27B-TTURBO-Cold-Fusion-709-L-Uncensored-mlx-oQ4e is the official Apple Silicon MLX release of DavidAU's Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored.

Engineered specifically for Apple Silicon Unified Memory architectures (M2, M3, M4, M5 Max / Ultra), this release couples oQ4e imatrix-guided mixed-precision with Solstice's proprietary 10-Level Cognitive Variable Thinking System and native Hardware Multi-Token Prediction (MTP).

Model Architecture Specifications:

• Base Precision: oQ4e mixed-precision with importance-matrix sensitivity allocation.
• Sensitive Layer Protection: Attention QKVO projections, MLP gate/up matrices, and normalization tensors strictly preserved at higher bit-depths (6-bit/8-bit/BF16).
• Multi-Token Prediction (MTP): Native Hardware MTP execution on Metal, providing 58–74 tok/s on consumer and pro Apple Silicon.
• Context Scaling: 262,144 tokens native context window.
• Refusal Vector Neutralization: Full orthogonalized abliteration across all safety refusal directions.

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Official ARC-709 Benchmark Scoreboard

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Solstice 10-Level Cognitive Variable Thinking Engine

This checkpoint embeds the Solstice 10-Level Cognitive Jinja chat template, allowing instant dynamic control over reasoning depth:

{REASON:mortal}    -> Level 0: 0 thinking tokens (instant instruct mode)
{REASON:apollo}    -> Level 2: 200–400 tokens (fast logic)
{REASON:athena}    -> Level 4: ~1,500 tokens (balanced synthesis)
{REASON:hyperion}  -> Level 7: Rigorous Qwen 3.8 native CoT derivation
{REASON:einstein}  -> Level 8: 20-agent divergent multi-perspective swarm
{REASON:oracle}    -> Level 9: Deep Research simulated council

Apple Silicon MLX Quickstart

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Solstice-AI/Qwen3.8-27B-TTURBO-Cold-Fusion-709-L-Uncensored-mlx-oQ4e")
response = generate(
    model,
    tokenizer,
    prompt="{REASON:apollo} Explain the mathematical advantage of mixed-precision oQ quantization on Apple Silicon unified memory.",
    max_tokens=2048,
    verbose=True
)
print(response)

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Hardware Sizing & Unified Memory Footprint

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Organization & Attribution

• Organization: Solstice AI (Solstice-AI)
• Base Model: DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored