Executive Overview
Solstice-AI/Qwen3.8-27B-TTURBO-Cold-Fusion-709-L-Uncensored-mlx-oQ8e-1M 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:
oQ8emixed-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: 1,048,576 tokens (1M native YaRN configuration).
- 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-1M")
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
- Organization: Solstice AI (
Solstice-AI) - Base Model: DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored