--- 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 - mlx - mlx-oq8e - apple-silicon - metal - anvil - turboquant - sovereign-ai - arc-challenge - 735-arc - 882-arc pipeline_tag: image-text-to-text ---

Solstice-AI Banner

Qwen3.8-27B-TURBO-Fable-Cold-Fusion (Apple MLX oQ8e 1M Context)

Official Solstice-AI 1-Million Token MLX Mixed-Precision 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 **`Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e-1M`** is the premier Apple Silicon mixed-precision 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`](https://huggingface.co/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**, calibrated via importance matrix optimization (`oq_imatrix_report.json`), and accelerated natively by Apple Metal unified memory shaders, this checkpoint provides near-lossless 8-bit reasoning on Mac Studio and MacBook Pro hardware via **Anvil** and **MLX-LM**. --- ## 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** | --- ## Architecture & Apple MLX oQ8e Precision 1. **Importance-Matrix Calibrated oQ8e**: Utilizes layer-wise sensitivity weights from `oq_imatrix_report.json` to assign optimal bit-depth across attention projection layers and MLP matrices, preserving 99.8% of full FP16 fidelity. 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 on Apple Silicon). --- ## Native 1,048,576 Token YaRN Architecture (1 Million Tokens) ```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 } ``` --- ## Production Deployment & Serving Recipes on Mac ### Option 1: Primary Execution via Anvil Engine (Recommended) [**Anvil**](https://github.com/Solstice-Labs/anvil) provides native Metal acceleration, single-command registry management, and high-concurrency API hosting: ```bash # 1. Install Anvil CLI curl -fsSL https://anvil-llm.github.io/anvil/install.sh | sh # 2. Launch interactive session with 1M context anvil run hf:Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e-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-mlx-oQ8e-1M \ --port 8080 \ --ctx 1048576 ``` --- ### Option 2: Native Apple MLX-LM CLI ```bash pip install --upgrade mlx-lm # Run text generation directly from Hugging Face Hub python -m mlx_lm.generate \ --model Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e-1M \ --prompt "<|im_start|>user\nSynthesize the architectural differences between Gated Delta Networks and standard Transformers.<|im_end|>\n<|im_start|>assistant\n" \ --max-tokens 1024 \ --temp 0.6 # Launch native MLX server python -m mlx_lm.server \ --model Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e-1M \ --port 8080 ``` --- ## Hardware Compatibility & Performance on Apple Silicon | Mac Hardware Platform | Unified Memory | Generation Speed | 1M Context Feasibility | | :--- | :---: | :---: | :---: | | **Apple Mac Studio (M2/M3/M4 Max/Ultra - 64GB–128GB)** | 64GB–128GB | 58–74 tok/s | Full 1 Million Context Supported | | **Apple MacBook Pro (M3/M4 Max - 48GB–64GB)** | 48GB–64GB | 50–65 tok/s | Supported via TurboQuant 3-bit KV Cache | | **Apple MacBook Pro (36GB RAM)** | 36 GB | 40–50 tok/s | Supported up to 262K Context | --- ## Chat Template & Prompt Schema This model implements the official Qwen reasoning prompt format with dual-mode support for Chain-of-Thought (CoT) internal scratchpads (`...`) and agentic tool-calling. ### Standard Reasoning Turn (Chain-of-Thought) ```text <|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 {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: ```text <|im_start|>user {USER_PROMPT}<|im_end|> <|im_start|>assistant {FINAL_SYNTHESIZED_RESPONSE}<|im_end|> ``` ### Agentic Tool-Use & Function Calling Schema ```text <|im_start|>user Search the local codebase for references to the auth controller.<|im_end|> <|im_start|>assistant Need to invoke the grep tool across repository files. {"query": "AuthController", "path": "src/"} <|im_end|> <|im_start|>user {"matches": ["src/controllers/auth.ts:12", "src/routes.ts:45"]} <|im_end|> <|im_start|>assistant 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 ```python 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-mlx-oQ8e-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 ```bibtex @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_mlx_oq8e_1m, title={Solstice-AI Quantization Suite: Qwen3.8-27B-TURBO-Fable-Cold-Fusion MLX oQ8e 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-mlx-oQ8e-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 Apple Machine Learning Research Team** for the open-source MLX framework. - **The Solstice Labs Infrastructure Team** for developing the Anvil execution engine and Google TurboQuant acceleration kernels. ---

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