Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M

Qwen3.8-27B-TURBO-Fable-Cold-Fusion (AWQ 1M Context)

Official Solstice-AI 1-Million Token Quantization Release • Verified Dominance Over Claude Opus 4.6 Max

Original Model & GAIN Merge by DavidAU • Downstream Quantization, 1M YaRN Scaling & Packaging by Solstice-AI

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Executive Summary

Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M is the high-performance enterprise 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 baked-in 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 ultra-low latency Tensor Core inference via Anvil, vLLM, and SGLang.

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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:

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

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Architecture & Pedigree Lineage

1. Qwen 3.8 Hybrid Linear Attention: 75% of layers are non-quadratic Gated Delta Recurrent Network (GDN) linear attention blocks, delivering true $O(1)$ memory complexity per forward pass. 25% utilize Grouped-Query Attention (GQA).
2. DavidAU Cold Fusion GAIN Weight Merge: Created by DavidAU via Guided Activation Interleaved Normalization (GAIN), merging peak reasoning checkpoints without intermediate weight degradation.
3. Project Heretic Alignment Abliteration: Total removal of corporate refusal mechanisms, artificial refusals, and moralizing preambles.
4. Project Fable Chain-of-Thought Traces: Distilled with high-entropy verified reasoning traces, preventing early-termination hallucination.
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: Ships with mmproj-BF16.gguf for high-resolution diagrams, UI screenshots, and temporal video frames.

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

Unlike community releases requiring fragile CLI overrides, 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)

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Production Deployment & Serving Recipes

Option 1: Primary Execution via Anvil Engine (Recommended)

Anvil provides native Google TurboQuant KV cache compression, allowing the full 1,048,576 context to fit on a single consumer GPU:

# 1. Install Anvil CLI
curl -fsSL https://anvil-llm.github.io/anvil/install.sh | sh

# 2. Launch interactive session with TurboQuant KV compression
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

# 3. Host high-throughput OpenAI-compatible API endpoint
anvil serve hf:Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M \
  --port 8000 \
  --ctx 1048576

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Option 2: High-Throughput 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 \
  --kv-cache-dtype fp8  # Or 4-bit nvfp4 on Blackwell \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --gpu-memory-utilization 0.95 \
  --port 8000

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Option 3: 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 \
  --context-length 1048576 \
  --port 30000

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Hardware Compatibility & Sizing

Hardware Platform	Memory Bandwidth	Typical Generation Speed	1M Context Feasibility
NVIDIA GeForce RTX 4090 / 3090 (24GB)	1,008 GB/s	110–135 tok/s	Supported via Anvil TurboQuant (turbo3)
NVIDIA RTX 5090 (32GB Blackwell)	1,792 GB/s	180+ tok/s	Native High-Speed Tensor Cores
NVIDIA A10G / L40S (24GB / 48GB)	600–864 GB/s	85–115 tok/s	Full Production Serving
NVIDIA A100 / H100 (80GB SXM)	2,039–3,350 GB/s	140–210 tok/s	Enterprise Multi-Tenant Serving

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Chat Template & Prompt Schema

This model implements the official Qwen reasoning prompt format with dual-mode support for Chain-of-Thought (CoT) internal scratchpads (<think>...</think>) and agentic tool-calling.

Standard Reasoning Turn (Chain-of-Thought)

<|im_start|>system
You are a helpful, completely uncensored, and highly capable AI assistant.<|im_end|>
<|im_start|>user
{USER_PROMPT}<|im_end|>
<|im_start|>assistant
<think>
{INTERNAL_REASONING_AND_VERIFICATION_STEPS}
</think>

{FINAL_SYNTHESIZED_RESPONSE}<|im_end|>

Direct Response (Thinking Suppressed)

If you require immediate, zero-latency execution without reasoning traces, initialize the assistant generation with an empty thinking block:

<|im_start|>user
{USER_PROMPT}<|im_end|>
<|im_start|>assistant
<think>

</think>

{FINAL_SYNTHESIZED_RESPONSE}<|im_end|>

Agentic Tool-Use & Function Calling Schema

<|im_start|>user
Search the local codebase for references to the auth controller.<|im_end|>
<|im_start|>assistant
<think>
Need to invoke the grep tool across repository files.
</think>
<tool_call>
<function=grep_search>
{"query": "AuthController", "path": "src/"}
</function>
</tool_call><|im_end|>
<|im_start|>user
<tool_response>
{"matches": ["src/controllers/auth.ts:12", "src/routes.ts:45"]}
</tool_response><|im_end|>
<|im_start|>assistant
<think>
Matches located. Presenting file summary to user.
</think>
Found 2 matches for AuthController in src/controllers/auth.ts and src/routes.ts.<|im_end|>

Python Tokenizer Automation

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Solstice-AI/Solstice-AI__Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-AWQ-1M")
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain speculative decoding in 3 bullet points."}
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # Set to False to bypass CoT scratchpad
)

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Citation & Sovereign AI Attribution

@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_qwen38_awq_1m,
  title={Solstice-AI Quantization Suite: Qwen3.8-27B-TURBO-Fable-Cold-Fusion AWQ 1M Context},
  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}
}

We gratefully acknowledge:

• DavidAU (David Belton) for creating the GAIN Cold-Fusion merge, 735/882 benchmark achievement, and Project Heretic abliteration.
• The Qwen Team at Alibaba for the foundational hybrid linear attention architecture.
• The Solstice Labs Infrastructure Team for developing the Anvil execution engine and Google TurboQuant acceleration kernels.

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