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 INT4 1M Context)

Official Solstice-AI AWQ INT4 Release • 1M Token Context via YaRN • Verified Dominance Over Claude Opus 4.6 Max

Original Model & GAIN Merge by DavidAU • Downstream Quantization, 1M Context Extension & 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 AWQ INT4 quantized 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), extended to 1,048,576 tokens (1M) context via YaRN RoPE scaling.

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, this checkpoint is calibrated for universal cross-vendor hardware execution across AMD ROCm, Intel Gaudi, and modern Tensor Core architectures via vLLM.

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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 & AWQ INT4 Quantization

1. AWQ INT4 Quantization: Activation-aware Weight Quantization (AWQ) preserving 4-bit weights with critical layers kept in higher precision. 27B model compressed from ~54 GB BF16 to ~18 GB INT4.
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. Native 1,048,576 Token YaRN Architecture (1 Million Tokens): RoPE scaling factor 4.0x extending from the native 262,144 token context to 1,048,576 tokens.

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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

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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)
FP8 KV Cache (2x compression):  44.2 GB VRAM (Single A100 80GB)
TurboQuant 4-bit KV (3.8x):    23.3 GB VRAM (Single RTX 5090 32GB)
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: Universal Execution via vLLM (Primary)

This checkpoint uses the AWQ quantization format. vLLM's built-in AWQ Marlin backend handles it directly — pass --quantization awq:

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 turboquant_4bit_nc \
  --gpu-memory-utilization 0.95 \
  --dtype auto \
  --enable-prefix-caching \
  --trust-remote-code \
  --port 8000

Key flags:

• --quantization awq — required. Tells vLLM to use the AWQ Marlin backend for INT4 weight dequantization.
• --max-model-len 1048576 — 1M token YaRN-extended context. Reduce if you have less VRAM.
• --kv-cache-dtype turboquant_4bit_nc — stock vLLM 0.20+ native TurboQuant preset: 3.8× KV cache compression, +2.71% PPL (near-lossless in practice for chat/reasoning). Enables 1M context to fit on a 24–32 GB consumer card. This is the recommended setting for 1M context on single-GPU consumer hardware.
• Alternative KV cache: --kv-cache-dtype fp8 works too (2× compression, ~44 GB KV cache at 1M context) — use if you have 80 GB of VRAM and want maximum quality. On Blackwell you can also try --kv-cache-dtype turboquant_4bit_nc.
• --attention-backend flashinfer — recommended for best latency on supported hardware.
• --gpu-memory-utilization 0.95 — high utilization needed for 1M context; reduce to 0.85 if you hit OOM on smaller cards.
• --trust-remote-code — this model has a custom tokenizer/chat template.

For DGX Spark / GB10 specifically (SM121), see the community DGX Spark + vLLM playbook — some additional env vars may be needed depending on your vLLM version/container.

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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>...<>) 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}
<>

{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>
<>

{FINAL_SYNTHESIZED_RESPONSE}<|im_end|>

Agentic Tool-Use & Function Calling Schema

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File Manifest

File	Size	Description
model.safetensors	~18 GB	AWQ INT4 quantized weights
model.safetensors.index.json	~300 KB	Safetensors index
model-mtp-restored.safetensors	~849 MB	MTP speculative draft head (BF16, 15 tensors)
config.json	—	Model config (AWQ + text_config)
tokenizer.json	—	Qwen3.8 tokenizer
tokenizer_config.json	—	Tokenizer config
chat_template.jinja	—	Chat template (Qwen3.8 reasoning format)
generation_config.json	—	Generation defaults

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Citation

@misc{solstice-ai-qwen38-27b-awq-1m,
  title={Solstice-AI Quantization Suite: Qwen3.8-27B-TURBO-Fable-Cold-Fusion AWQ INT4 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-AWQ-1M}
}

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