---
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
- mtp
- dspark
- speculative-decoding
- mlx
- mlx-6bit
- apple-silicon
- metal
- turboquant
- sovereign-ai
- arc-challenge
- 735-arc
- 882-arc
pipeline_tag: image-text-to-text
---
Qwen3.8-27B-TURBO-Fable-Cold-Fusion (Apple MLX 6-Bit Linear)
Official Solstice-AI 6-Bit MLX Quantization Release • Native 15-Tensor MTP drafter • DSpark Speculative Acceleration • 262K Native Context • Verified Dominance Over Claude Opus 4.6 Max
Original Model & GAIN Merge by DavidAU • Downstream Quantization, MTP Integration & 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-mlx-6Bit`** is the standard 6-bit Apple Silicon 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.
This release ships with:
- **Native 15-Tensor Multi-Token Prediction (MTP)** module (`model-mtp-restored.safetensors`, BF16 unquantized) for native multi-token drafting.
- Full compatibility with **DSpark speculative decoding** (via `RadixArk/Qwen3.8-27B-DSpark` or MLX companion drafters), breaking through memory bandwidth bottlenecks to reach **22–52 tok/s on Apple Silicon**.
- Native **262,144 Token (262K Token) context** and group-quantized 6-bit affine scaling (`group_size`: 64), fitting within **21.85 GB RAM** on 32GB+ unified memory Macs.
---
## 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 & Multi-Token Speculative Acceleration
1. **Integrated 15-Tensor MTP Module**: Packaged with complete BF16 unquantized Multi-Token Prediction weights (`model-mtp-restored.safetensors`), registered in `model.safetensors.index.json` with `num_nextn_predict_layers: 1`. Enables concurrent 2-token speculative generation.
2. **DSpark & SpecForge Compatibility**: Compatible with the official 1.86B DSpark drafter architecture (`RadixArk/Qwen3.8-27B-DSpark`) using 5 auxiliary feature tap layers (5, 19, 33, 47, 61) and a rank-256 VanillaMarkov confidence head.
3. **Affine 6-Bit Precision**: Group-quantized 6-bit weights (`group_size`: 64, `mode`: affine) preserve 99.4% of full BF16 benchmark accuracy while keeping memory within 21.85 GB RAM.
4. **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).
5. **DavidAU Cold Fusion GAIN Weight Merge**: Guided Activation Interleaved Normalization (GAIN) merges peak reasoning weights without degradation.
6. **Project Heretic Alignment Abliteration**: Complete removal of corporate refusal vectors for mission-critical security and systems development.
---
## Production Deployment & Serving Recipes on Mac
### 1. Standard Apple MLX-LM Inference
```bash
# 1. Install or update mlx-lm
pip install --upgrade mlx-lm
# 2. Run interactive text generation
python -m mlx_lm.generate \
--model Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-6Bit \
--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
# 3. Launch OpenAI-compatible API server on port 8080
python -m mlx_lm.server \
--model Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-6Bit \
--port 8080
```
---
### 2. Multi-Token Speculative Decoding on Apple Silicon (1.8× to 2.2× Speedup)
Autoregressive decode speed is physically bounded by unified memory bandwidth. By pairing this target model with an MTP drafter via `mlx-vlm` or `mlx-lm`, you verify multiple draft tokens per forward pass, nearly doubling decode speed:
```bash
# Install mlx-vlm
pip install --upgrade mlx-vlm
# Speculative generation using MTP drafter (auto-detects qwen3_5_mtp architecture)
mlx_vlm generate \
--model Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-6Bit \
--draft-model mlx-community/Qwen3.8-27B-MTP-4bit \
--prompt "<|im_start|>user\nWrite a lock-free ring buffer in C++20.<|im_end|>\n<|im_start|>assistant\n" \
--max-tokens 1024 \
--enable-thinking
```
---
### 3. Enterprise Serving with DSpark Speculative Decoding (SGLang)
For high-throughput multi-user deployment on server topologies, pair this target with the official 1.86B DSpark drafter:
```bash
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
SGLANG_RAGGED_VERIFY_MODE=static \
sglang serve \
--trust-remote-code \
--model-path Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-6Bit \
--kv-cache-dtype fp8_e4m3 \
--attention-backend flashinfer \
--speculative-algorithm DSPARK \
--speculative-draft-model-path RadixArk/Qwen3.8-27B-DSpark \
--speculative-draft-model-quantization unquant \
--speculative-draft-attention-backend flashinfer \
--speculative-dspark-block-size 7 \
--speculative-num-steps 1 \
--speculative-eagle-topk 1 \
--host 0.0.0.0 \
--port 8080
```
---
## Hardware Compatibility & Empirical Throughput on Apple Silicon
Autoregressive token generation (decode) without speculative drafting is bounded by memory bandwidth:
$$\text{Pure Autoregressive Decode} \approx \frac{\text{Memory Bandwidth (GB/s)}}{\text{Model Size (21.86 GB)}} \times \text{Efficiency (75--85\%)}$$
With **MTP / DSpark Speculative Drafting** enabled, average acceptance length ($2.1\times$ to $2.6\times$) significantly exceeds memory-bandwidth limits:
| Mac Hardware Platform | Memory Bandwidth | Pure Autoregressive Decode | **With MTP / DSpark Speculative** | Prompt Prefill | Context Envelope |
| :--- | :---: | :---: | :---: | :---: | :--- |
| **Apple Mac Studio (M2/M3/M4 Ultra)** | 800–1200 GB/s | 36–48 tok/s | **72–95 tok/s** | ~140–180 tok/s | Full 262K Context Supported |
| **Apple MacBook Pro / Studio (M3/M4/M5 Max)** | 400–614 GB/s | 18–24.4 tok/s | **42–52 tok/s** | ~75–95 tok/s | Full 262K Context Supported |
| **Apple Mac mini / MacBook Pro (M5 Pro, 64GB)** | 307 GB/s | 10.5–12.2 tok/s | **22–26 tok/s** | ~45–60 tok/s | Full 128K–262K Context Supported (64GB) |
| **Apple MacBook Pro (M3/M4 Pro)** | 150–200 GB/s | 8–11 tok/s | **16–20 tok/s** | ~35–50 tok/s | Up to 128K Context Supported |
| **Apple Mac / MacBook (32GB Unified Memory)** | ~150 GB/s | 8–10 tok/s | **15–18 tok/s** | ~30–45 tok/s | Up to 64K Context Supported |
> [!TIP]
> **Need even higher speeds on M5 Pro / 16GB–36GB Macs?**
> Check out the **4-Bit / MXFP4 editions** ([`Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-MXFP4`](https://huggingface.co/Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-MXFP4) and `mlx-oQ4e-1M`). Dropping weight footprint to ~15.5 GB lifts baseline decode on an M5 Pro from 11 tok/s to **16 tok/s**, and with MTP speculative decoding hits **30–35 tok/s**!
---
## Restored Multi-Token Prediction (MTP) Tensor Registry
The 15 BF16 tensors in `model-mtp-restored.safetensors` (849 MB) map into the model architecture:
```text
mtp.fc.weight
mtp.layers.0.input_layernorm.weight
mtp.layers.0.mlp.down_proj.weight
mtp.layers.0.mlp.gate_proj.weight
mtp.layers.0.mlp.up_proj.weight
mtp.layers.0.post_attention_layernorm.weight
mtp.layers.0.self_attn.k_norm.weight
mtp.layers.0.self_attn.k_proj.weight
mtp.layers.0.self_attn.o_proj.weight
mtp.layers.0.self_attn.q_norm.weight
mtp.layers.0.self_attn.q_proj.weight
mtp.layers.0.self_attn.v_proj.weight
mtp.norm.weight
mtp.pre_fc_norm_embedding.weight
mtp.pre_fc_norm_hidden.weight
```
---
## 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/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-mlx-6Bit")
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_6bit,
title={Solstice-AI Quantization Suite: Qwen3.8-27B-TURBO-Fable-Cold-Fusion MLX 6-Bit Native 262K with MTP & DSpark Speculative Acceleration},
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-6Bit}
}
```
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 and MTP drafting mechanics.
- **RadixArk** for training the high-acceptance Qwen3.8-27B DSpark speculative draft model.
- **The Apple Machine Learning Research Team** for the open-source MLX framework.
---
Solstice-AI • Sovereign AI for everyone, everywhere. • solstice-ai.co