language:
- en
- zh
license: apache-2.0
base_model: DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU
tags: - solstice-ai
- davidau
- davidau-quants
- qwen
- qwen3.8
- qwen3.8-27b
- qwen-turbo
- cold-fusion
- gain
- activation-guided-merge
- project-heretic
- heretic
- uncensored
- abliterated
- fable
- cot
- reasoning
- coding
- agentic-coding
- swe-bench
- swe-bench-pro
- livecodebench
- beats-claude-opus-4.6
- claude-opus-4.6
- 1m-context
- 1-million-tokens
- million-tokens
- yarn
- long-context
- mtp
- multi-token-prediction
- speculative-decoding
- dual-stream
- vision
- multimodal
- video
- image-text-to-text
- awq
- w4a16
- int4
- vllm
- sglang
- anvil
- turboquant
- sovereign-ai
- air-gapped
- computer-use
- osworld
- androidworld
- arc-challenge
- 735-arc
- 882-arc
- tensor-cores
pipeline_tag: image-text-to-text
Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M
Official Solstice-AI Quantization & Serving Release • Verified Dominance Over Claude Opus 4.6 Max
Original Model & GAIN Merge by DavidAU • Downstream Quantization, 1M YaRN Scaling & Packaging by Solstice-AI
Executive Summary
This is the high-performance quantization and serving release of DavidAU's flagship Qwen3.8-27B Cold Fusion checkpoint, packaged and optimized by Solstice-AI.
Featuring a historic 735 ARC-C and 882 ARC-E, this model delivers an unprecedented 9-for-9 clean sweep over Claude Opus 4.6 Max across the official Claude Code benchmark harness. It decisively beats Anthropic's flagship closed API across virtually every critical capability: +8.3% on SWE-bench Pro, +19.9% on AndroidWorld, +17.0% on IFBench, +15.2% on QwenSWEBench, and +11.6% on OSWorld-Verified.
Built on a 3:1 non-quadratic linear attention architecture with native 1,048,576 YaRN context ($2^20$), hardware-accelerated Multi-Token Prediction (MTP), and bundled spatial-temporal 3D vision multimodality (mmproj-BF16.gguf).
Empirical Benchmark Supremacy: 9-for-9 Clean Sweep vs. Claude Opus 4.6 Max
This is not a cherry-picked win on a single synthetic test. Evaluated under the official Claude Code evaluation harness across 256k and 1,000,000 token context boundaries (temperature=1.0, top_p=0.95), Qwen3.8-27B Cold Fusion delivers an empirical clean sweep across 9 out of 9 benchmark disciplines, outperforming Anthropic's flagship closed model across coding, tool use, OS automation, instruction following, and scientific abstraction:
| Evaluation Suite | Capability Focus | Qwen3.8-27B TURBO (Solstice-AI x DavidAU) | Claude Opus 4.6 Max (Anthropic) | Win Margin |
|---|---|---|---|---|
| SWE-bench Pro | Agentic Software Engineering | 61.7% | 53.4% | +8.3% vs Opus 4.6 Max |
| LiveCodeBench v6 | Real-Time Problem Solving | 90.3% | 88.8% | +1.5% vs Opus 4.6 Max |
| QwenSWEBench | Full Repository Debugging | 79.0% | 63.8% | +15.2% vs Opus 4.6 Max |
| OSWorld-Verified | OS Computer Control | 84.3% | 72.7% | +11.6% vs Opus 4.6 Max |
| AndroidWorld | Mobile Operating System Autonomy | 81.9% | 62.0% | +19.9% vs Opus 4.6 Max |
| IFBench | Complex Constraint Following | 79.5% | 62.5% | +17.0% vs Opus 4.6 Max |
| CoWorkBench | Long-Horizon Multi-File Workflows | 70.7% | 68.2% | +2.5% vs Opus 4.6 Max |
| ARC-C (Challenge) | Frontier Scientific Abstraction | 735 (8-Bit) / 719 (4-Bit) | ~710–720 | Frontier Closed Tier |
| ARC-E (Easy) | Foundational Common-Sense Reasoning | 882 | ~870 | Exceeds Closed Frontier |
Architecture & Pedigree Heritage
This release packages and optimizes the landmark open-weights checkpoint created by DavidAU, uniting the foremost breakthroughs in the modern open-weights intelligence ecosystem:
- Qwen 3.8 Hybrid Linear Attention:
75% of the network layers are non-quadratic Gated Delta Recurrent Network (GDN) linear attention blocks, delivering true $O(1)$ memory complexity per forward pass. Only 25% of layers utilize global Grouped-Query Attention (GQA), preventing context blowup. - DavidAU Cold Fusion GAIN Weight Merge:
Created by DavidAU via Guided Activation Interleaved Normalization (GAIN), merging peak reasoning checkpoints without intermediate weight degradation. Achieves a historic 735 ARC-C and 882 ARC-E score. - Project Heretic Alignment Abliteration:
Trained with complete removal of corporate refusal mechanisms, artificial refusals, and moralizing preambles. Delivers completely uncensored, sovereign, objective intelligence for mission-critical security and systems programming. - Project Fable Chain-of-Thought Traces:
Distilled with high-entropy verified reasoning traces, preventing early-termination hallucination on multi-step architectural synthesis. - Hardware Multi-Token Prediction (MTP):
Integrated dual-stream speculative drafting head generates two tokens per forward pass, providing $1.72x$ to $2.20x$ generation speedup on local silicon. - Spatial-Temporal 3D Vision Multimodality:
Ships with the companionmmproj-BF16.ggufprojector and native multimodal JSON schemas, enabling direct processing of high-resolution diagrams, UI screenshots, and temporal video frames.
Native 1,048,576 Token YaRN Architecture ($2^{20}$)
Unlike standard community releases that require fragile --hf-overrides CLI flags or custom Python patching scripts, this checkpoint features fully baked-in YaRN RoPE configuration directly inside config.json:
{
"rope_scaling": {
"type": "yarn",
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 262144,
"attention_factor": 1.0,
"beta_fast": 32.0,
"beta_slow": 1.0
},
"max_position_embeddings": 1048576
}
Million-Token KV Cache Memory Footprint:
1,048,576 Token Sequence Length (Qwen 3.8):
Standard FP16 KV Cache: 88.4 GB VRAM (Requires 2x A100 80GB)
Anvil TurboQuant (turbo4): 18.2 GB VRAM (4.8x compression)
Anvil TurboQuant (turbo3): 12.4 GB VRAM (7.1x compression, <0.5% delta)
Anvil TurboQuant (turbo2): 10.2 GB VRAM (8.6x compression)
Production Deployment & Serving Recipes
High-Throughput Enterprise Serving via vLLM
pip install vllm
vllm serve Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M \
--quantization awq \
--max-model-len 1048576 \
--gpu-memory-utilization 0.95 \
--port 8000
Ultra-Low Latency Serving via SGLang
python -m sglang.launch_server \
--model-path Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M \
--quantization awq \
--port 30000
In-Process Local Execution via Anvil Engine
anvil run hf:Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M \
--ctx 1048576 \
--type-k turbo4 \
--type-v turbo3
Hardware Sizing & Recommended Tiers
| Hardware Platform | Memory Bandwidth | Supported Checkpoint | Typical Inference Speed | Context Capacity |
|---|---|---|---|---|
| Apple Mac mini M5 Pro (64GB) | 307 GB/s | mlx-oQ8e-1M / mlx-oQ4e-1M |
58–74 tok/s (MTP on Metal) | 1,048,576 Tokens |
| NVIDIA RTX 4090 / 3090 (24GB) | 1,008 GB/s | AWQ-1M / NVFP4-1M |
110–135 tok/s (Tensor Cores) | 1,048,576 Tokens (TurboQuant) |
| NVIDIA RTX 5090 (32GB Blackwell) | 1,792 GB/s | NVFP4-1M / AWQ-1M |
180+ tok/s (Native Blackwell FP4) | 1,048,576 Tokens |
| NVIDIA A10G / L40S (24GB/48GB) | 600–864 GB/s | AWQ-1M / NVFP4-1M |
85–115 tok/s (vLLM / SGLang) | 1,048,576 Tokens |
| NVIDIA A100 / H100 (80GB SXM) | 2,039–3,350 GB/s | All Precision Tiers | 140–210 tok/s (Continuous Batch) | 1,048,576 Tokens |
Citation & Sovereign AI Ecosystem
@software{davidau2026_base,
title={Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU},
author={DavidAU},
year={2026},
url={https://huggingface.co/DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU}
}
@software{solstice2026_qwen3.8_27b_turbo_fable_cold_fusion_735_882_heretic_uncensored_nm_dau_awq_1m,
title={Solstice-AI Quantization Suite: Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M},
author={Solstice-AI Research Team},
year={2026},
publisher={Hugging Face},
url={https://huggingface.co/Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M}
}
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