Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-1M

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
• nvfp4
• fp4
• blackwell
• rtx-5090
• vllm
• sglang
• ultraoptimised
• ultraefficient
• efficient
• arc-challenge
• 735-arc
• 882-arc
• image-text-to-text
• conversational
• en
• zh pipeline_tag: image-text-to-text

Executive Summary

Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-1M is the native 1M token context NVIDIA Blackwell-optimized 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), extended to 1,048,576 tokens 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 NVIDIA Blackwell NVFP4 4-bit microscaling, 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-high throughput execution on NVIDIA Blackwell (B200, GB200, RTX 5090) silicon.

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

Evaluated under the official Claude Code evaluation harness across 256k 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 & NVIDIA NVFP4 Microscaling

1. NVFP4 Microscaling Format: Leverages NVIDIA Blackwell 4-bit floating-point Tensor Cores with two-level microscaling factors, delivering double the compute throughput of standard INT4 GEMM with floating-point dynamic range.
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. YaRN 1M Context Extension: RoPE scaling extends the native 262K context to 1,048,576 tokens with stable attention performance at long ranges.
7. Spatial-Temporal 3D Vision Multimodality: Bundled with mmproj-BF16.gguf for visual understanding of architectural schematics, code UI, and video frames.

Production Deployment & Serving Recipes

vLLM: High-Speed Blackwell NVFP4 Serving (Primary)

This checkpoint uses the compressed-tensors format. vLLM auto-detects it from config.json — no --quantization flag is required (passing --quantization modelopt_fp4 will error because this is not a ModelOpt checkpoint).

pip install vllm

vllm serve Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-1M \
  --max-model-len 1048576 \
  --kv-cache-dtype turboquant_4bit_nc \
  --gpu-memory-utilization 0.95 \
  --dtype auto \
  --attention-backend flashinfer \
  --enable-prefix-caching \
  --trust-remote-code \
  --port 8000

Key flags:

• No --quantization flag — vLLM reads config.json, sees quant_method: compressed-tensors, and picks the right loader automatically.
• --max-model-len 1048576 — 1M token context (YaRN extension). Only use full 1M if you need it; shorter --max-model-len saves KV cache memory.
• --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 Blackwell card. The README previously listed --kv-cache-dtype nvfp4 which is incorrect for vLLM's KV cache path on consumer Blackwell.
• --attention-backend flashinfer — required for NVFP4 weight kernels on Blackwell.
• --trust-remote-code — this model has a custom tokenizer/chat template.
• --gpu-memory-utilization 0.95 — high utilization needed for 1M context; reduce if you hit OOM.

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.

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

<|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.
<>
<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.
<>
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/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-1M")
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
)

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_nvfp4_1m,
  title={Solstice-AI Quantization Suite: Qwen3.8-27B-TURBO-Fable-Cold-Fusion NVFP4 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-NVFP4-1M}
}

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.
• NVIDIA ModelOpt & TensorRT Teams for Blackwell microscaling quantization standards.