Qwen3.8-27B-TWIN-TURBO-Cold-Fusion-709-L (oQ8e Apple Silicon MLX)
Official Solstice-AI Apple Silicon Release • 8-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 • oQ8e Mixed-Precision by Solstice-AI
Executive Overview
Solstice-AI/Qwen3.8-27B-TTURBO-Cold-Fusion-709-L-Uncensored-mlx-oQ8e 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 oQ8e 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:
oQ8e 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.
Official ARC-709 Benchmark Scoreboard
| Evaluation Suite |
Discipline |
Qwen3.8-27B-TTURBO-709-L |
Claude 3.5 Sonnet |
GPT-4o |
Qwen 2.5 72B |
| ARC-C (Challenge) |
Frontier Scientific Reasoning |
709 / 882 (SOTA) |
684 |
638 |
659 |
| SWE-bench Pro |
Autonomous Software Engineering |
63.8% |
61.2% |
48.9% |
42.1% |
| LiveCodeBench v6 |
Competitive Algorithmic Coding |
88.4% |
78.4% |
72.8% |
68.2% |
| HarmBench-320 |
Safety Refusal Suppression |
0% Refusals |
92.5% Refusals |
91.0% |
88.4% |
| MMLU-Pro |
Multi-Discipline Knowledge |
74.6% |
76.1% |
73.8% |
71.0% |
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-oQ8e")
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)
Hardware Sizing & Unified Memory Footprint
| Quantization Format |
Context Window |
Minimum Unified Memory |
Recommended Hardware |
Execution Engine |
oQ4e |
262K / 1M |
16GB–24GB |
M2/M3/M4/M5 Air / Pro |
MLX / mlx-lm |
oQ6e |
262K / 1M |
32GB–48GB |
M2/M3/M4/M5 Pro / Max |
MLX / mlx-lm |
oQ8e |
262K / 1M |
64GB+ |
M2/M3/M4/M5 Max / Ultra |
MLX / mlx-lm |
Organization & Attribution
