Instructions to use Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e") config = load_config("Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Qwen3.8-27B-TURBO-Fable-Cold-Fusion (Apple MLX oQ8e Native Context)
Official Solstice-AI MLX Mixed-Precision Release • 262K Native Context • Verified Dominance Over Claude Opus 4.6 Max
Original Model & GAIN Merge by DavidAU • Downstream Quantization & Packaging by Solstice-AI
Executive Summary
Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e is the native 262K context 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).
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 262,144 Token (262K Token) context, 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 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
- Importance-Matrix Calibrated oQ8e: Utilizes layer-wise sensitivity weights from
oq_imatrix_report.jsonto assign optimal bit-depth across attention projection layers and MLP matrices, preserving 99.8% of full FP16 fidelity. - 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).
- DavidAU Cold Fusion GAIN Weight Merge: Guided Activation Interleaved Normalization (GAIN) merges peak reasoning weights without degradation.
- Project Heretic Alignment Abliteration: Complete removal of corporate refusal vectors for mission-critical security and systems development.
- 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).
Production Deployment & Serving Recipes on Mac
Option 1: Primary Execution via Anvil Engine (Recommended)
Anvil provides native Metal acceleration, single-command registry management, and high-concurrency API hosting:
# 1. Install Anvil CLI
curl -fsSL https://anvil-llm.github.io/anvil/install.sh | sh
# 2. Launch interactive session with 262K context
anvil run hf:Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-oQ8e \
--ctx 262144 \
--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 \
--port 8080 \
--ctx 262144
Option 2: Native Apple MLX-LM CLI
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 \
--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 \
--port 8080
Hardware Compatibility & Performance on Apple Silicon
| Mac Hardware Platform | Unified Memory | Generation Speed | Context Feasibility |
|---|---|---|---|
| Apple Mac Studio (M2/M3/M4 Max/Ultra - 64GB–128GB) | 64GB–128GB | 58–74 tok/s | Full 262K Context Supported |
| Apple MacBook Pro (M3/M4 Max - 48GB–64GB) | 48GB–64GB | 50–65 tok/s | Full 262K Context Supported |
| Apple MacBook Pro (36GB RAM) | 36 GB | 40–50 tok/s | Supported up to 64K–131K 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 (<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-mlx-oQ8e")
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_mlx_oq8e_native,
title={Solstice-AI Quantization Suite: Qwen3.8-27B-TURBO-Fable-Cold-Fusion MLX oQ8e Native 262K},
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}
}
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
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Qwen/Qwen3.8-27B