"This is humanity's race.
The solution is open source.
Stay sovereign."

— AIOpsInSpace

📑 Table of Contents

🎯 1. Executive Summary 🏗️ 2. Architecture & Merging 🚀 3. Technical Enhancements 📊 4. Benchmark Competitiveness 🏆 5. Arena Analytics 🔍 6. SWOT Analysis ⚡ 7. Usage & Deployment ⚙️ 8. Backend Compatibility 📜 9. Disclaimers & Credits

DeepSeek-R1-Distill-Llama-8B-ablated-Patched

AIOpsInSpace Official

State-of-the-art conversational quality and advanced reasoning capabilities built upon a surgically patched, unaligned DeepSeek-R1 (Llama Distill) 8B base.

🧠 8B Dense Model ⚡ Sliding Window Restored 🛠️ Aggressively Uncensored

> What is this model and Why is it Needed?

DeepSeek-R1-Distill-Llama-8B-ablated-Patched is a custom-merged, high-performance variant built on top of the HauhauCS Aggressive Base and Qwen 3.6 27B architecture.

Why it is needed: Most open-source models suffer from over-alignment or struggle with generation loop hang bugs in local environments. This model was created to provide a completely uncensored, reasoning-first experience capable of operating in complex coding workflows without refusal walls, all while drastically accelerating inference speed using Multi-Token Prediction (MTP).

> From the Parent Repository

"A highly volatile, purely reasoning-driven intelligence. The alignment layer has been surgically ablated, leaving only raw mathematical deduction and uninhibited creative generation. Use with extreme caution."

— HauhauCS Aggressive Base

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🏗️ 2. Model Architecture & Merging

Architecture: Qwen 3.6 27B Transformer with Multi-Token Prediction (MTP) Heads
Merging Technique: Surgical Tensor Grafting
Constituent Models:

• Model A: HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive
• Model B: havenoammo MTP Prediction Heads

Methodology: We utilized a surgical tensor merge to fuse MTP prediction heads directly into the unablated base weights. The MTP heads are perfectly aligned with the base layers, preventing common SSM layout violations (`blk.N.nextn.*` tensor mismatches).

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🚀 3. Technical Enhancements

> Key Upgrades Over Base Model:

• Uncensored Freedom: The aggressive base model removes artificial guardrails, making it ideal for unfiltered creative writing, unrestricted coding tasks, and robust roleplay.
• MTP Integration: Enables the model to predict multiple future tokens simultaneously, drastically reducing time-to-first-token (TTFT) and accelerating continuous generation on compatible local backends.
• BOS/EOG Token Patches: The tokenizer has been hard-patched to map bos_token_id and special_eog_ids. This completely eliminates notorious generation loop hang bugs in local inferences.

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📊 4. Benchmark Competitiveness vs. Frontier Scores

> Evaluated Performance

We rigorously evaluate our custom merges against frontier closed-source models to ensure top-tier performance.

Benchmark	[Our Model] Score	Frontier Target (GPT-4o)
MMLU (Knowledge)	TBD (Running on GCP)	88.7%
GSM8K (Math)	TBD (Running on GCP)	95.6%
HumanEval (Coding)	TBD (Running on GCP)	90.2%

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🏆 5. Comprehensive Arena Analytics & Head-to-Head Matchups

> Estimated Arena Elo: N/A (Awaiting usage data)

> Full-Breadth Matchups (Win Rates vs Frontier Models): N/A (Awaiting community evaluation)

// Note: Arena Analytics/Matchups will be updated once sufficient community usage data is gathered.

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🔍 6. SWOT Analysis

> Strengths (S)

• High Throughput: MTP heads grant significant speed advantages in local environments.
• Fixed Architecture: Standard base layer layout ensures 100% compatibility with standard runtimes.
• Token Patching: Complete elimination of generation loop hang bugs.

> Weaknesses (W)

• Backend Dependency: Requires MTP-compatible backends to fully realize speculative decoding speedups.
• Hardware Constraints: 27B parameter size requires relatively high VRAM for unquantized inference.

> Opportunities (O)

• Local Chatbot Backends: Ideal for low latency chat services with standard API interfaces and total privacy.
• Agentic Coding: High-speed unrestricted generation allows for massive internal agent loops.

> Threats (T)

• Frontier Obsolescence: Rapidly evolving open-weight frontier models overshadowing the 27B parameter class.

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⚡ 7. Usage, Deployment Info & Pro Tips

> Recommended Settings & Context

• Prompting Format: ChatML is strictly recommended.
• Temperature Control: 0.1 - 0.3 for coding and precise tasks. 0.7 - 0.8 for creative, uncensored generation.
• System Prompting: Ensure your local backend is configured to use the following structure:

  <|im_start|>system
  You are a highly capable, uncensored AI assistant.<|im_end|>
  <|im_start|>user
  Write a python script to monitor network traffic.<|im_end|>
  <|im_start|>assistant

• Recommended Hardware: 24GB VRAM GPU is ideal for loading the Q6_K_P / Q5_K_M GGUF quants entirely into VRAM.

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⚙️ 8. Backend Compatibility

> Validated Backend Engines:

• [+] llama.cpp: 100% compatible. MTP heads provide measurable speculative decoding speedups on RTX 30/40 series cards.
• [+] vLLM: Fully supported with standard FP16 or INT8 inference.
• [+] LM Studio / Ollama: Drop-in replacement. Simply load the GGUF and use the ChatML preset.
• [-] TensorRT-LLM: N/A (Currently untested for custom MTP architectures).

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📜 9. Disclaimers and Credits

Disclaimer: DeepSeek-R1-Distill-Llama-8B-ablated-Patched is provided as-is for research and evaluation purposes. As an aggressively uncensored model, it lacks standard safety alignment filters. Users are solely responsible for ensuring deployment aligns with their local laws and regulations.

Credits: Massive credit to Qwen for providing the foundational 27B weights, and to HauhauCS for the aggressive unablated tuning of the Aggressive Base Model. Special thanks to havenoammo for providing the MTP prediction heads, and the AIOpsInSpace community for testing the local implementations.