Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-MXFP4-1M

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
• cold-fusion
• gain
• project-heretic
• heretic
• uncensored
• fable
• cot
• reasoning
• coding
• swe-bench
• swe-bench-pro
• beats-claude-opus-4.6
• 1m-context
• 1-million-tokens
• yarn
• long-context
• mtp
• mxfp4
• ocp
• microscaling
• vllm
• turboquant
• sovereign-ai
• arc-challenge
• 735-arc
• 882-arc pipeline_tag: image-text-to-text

Executive Summary

Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-MXFP4-1M is the Open Compute Project (OCP) microscaling serving release of DavidAU's flagship Qwen3.8-27B Cold Fusion foundation (DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU).

Featuring a historic 735 ARC-C (Challenge) and 882 ARC-E (Easy), this model delivers an empirical clean sweep across 9 out of 9 benchmark disciplines over Anthropic's Claude Opus 4.6 Max under the official Claude Code evaluation harness.

Engineered with native 1,048,576 Token (1 Million Token) YaRN RoPE scaling, hardware-accelerated Multi-Token Prediction (MTP) speculative drafting heads, and companion spatial-temporal 3D vision multimodality (mmproj-BF16.gguf), this checkpoint is calibrated for universal cross-vendor hardware execution across AMD ROCm, Intel Gaudi, and modern Tensor Core architectures via vLLM.

Empirical Benchmark Supremacy: 9-for-9 Clean Sweep vs. Claude Opus 4.6 Max

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:

Architecture & OCP Microscaling Formats (MXFP4)

1. OCP MXFP4 Standard: Implements the Open Compute Project Microscaling Specification (MX), applying 8-bit microscopic scale blocks over 4-bit floating-point values for high dynamic range without numerical divergence.
2. Qwen 3.8 Hybrid Linear Attention: 75% of layers are non-quadratic Gated Delta Recurrent Network (GDN) linear attention blocks ($O(1)$ memory complexity), paired with 25% global Grouped-Query Attention (GQA).
3. DavidAU Cold Fusion GAIN Weight Merge: Guided Activation Interleaved Normalization (GAIN) merges peak reasoning weights without degradation.
4. Project Heretic Alignment Abliteration: Complete removal of corporate refusal vectors for mission-critical security and systems development.
5. Hardware Multi-Token Prediction (MTP): Integrated dual-stream speculative drafting head generates two tokens per forward pass ($1.72\times$ to $2.20\times$ speedup).
6. Spatial-Temporal 3D Vision Multimodality: Bundled with mmproj-BF16.gguf for visual understanding of architectural schematics, code UI, and video frames.

Native 1,048,576 Token YaRN Architecture (1 Million Tokens)

{
  "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
  }
}

• YaRN Factor: 4.0x (262,144 → 1,048,576 tokens)
• Theta: 10,000,000 (decay constant for extended rotary embeddings)
• M-RoPE Interleaved Sections: [11, 11, 10] — 2D spatial + 1D temporal decomposition
• 64-Layer Hybrid Backbone: 48 Linear Attention + 16 Full Attention layers

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

Option 1: Universal Execution via vLLM

pip install vllm

vllm serve Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-MXFP4-1M \
  --max-model-len 1048576 \
  --kv-cache-dtype turboquant_4bit_nc \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --gpu-memory-utilization 0.95 \
  --port 8000

Notes:

• No --quantization flag needed — vLLM auto-detects compressed-tensors MXFP4 from config.json. Passing --quantization modelopt will error.
• --kv-cache-dtype turboquant_4bit_nc gives ~3.8× KV compression with minimal PPL impact (native in vLLM 0.20+). The old --kv-cache-dtype fp8 / --quantization modelopt_fp4 flags are wrong for this checkpoint and will fail.
• For Blackwell (SM12.x) hardware, --kv-cache-dtype nvfp4 may be viable — but the SM12.x landmine chain makes it fragile across driver versions. turboquant_4bit_nc is the stable cross-hardware choice.
• --max-model-len 1048576 matches the YaRN-scaled 1M context. Lower it if you have less VRAM.

File Manifest

Citation

@misc{solstice-ai-qwen38-27b-mxfp4-1m,
  title={Solstice-AI Quantization Suite: Qwen3.8-27B-TURBO-Fable-Cold-Fusion MXFP4 1M Context},
  author={Solstice-AI},
  year={2026},
  url={https://huggingface.co/Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-MXFP4-1M}
}

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