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AIOpsInSpace/
Dolphin-Mistral-24B-Venice-Edition-Patched

Name: Dolphin-Mistral-24B-Venice-Edition-Patched
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dphn/Dolphin-Mistral-24B-Venice-Edition

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"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

Dolphin-Mistral-24B-Venice-Edition-Patched
AIOpsInSpace Official
Dolphin 24B Venice Edition patched to restore sliding window attention and eliminate context fragmentation.

🎭 24B Dense Model
⚡ Sliding Window Restored
🛠️ Aggressively Uncensored
> What is this model and Why is it Needed?
Dolphin-Mistral-24B-Venice-Edition-Patched is built on top of the dphn/Dolphin-Mistral-24B-Venice-Edition base model.

Why it is needed: The original quantization suffered from lost sliding window attention parameters, causing context fragmentation and generation hangs beyond 4096 tokens. This patched release restores the full 32K context window and repairs tokenizer stop tokens.
> From the Parent Repository
"Uncensored, hyper-capable, and deeply conversational — Dolphin Venice Edition represents the crest of open source dialogue."


— Cognitive Computations / Dolphin Project

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🏗️ 2. Model Architecture & Merging

Architecture: Mistral 24B Transformer Architecture with Sliding Window Attention

Merging Technique: Attention Parameter Restoration & Tokenizer Patching

Constituent Models:
Base Model: [object Object]

Methodology: Restored missing sliding_window keys in GGUF metadata header and hard-patched tokenizer special tokens.

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🚀 3. Technical Enhancements

> Key Upgrades Over Base Model:
32K Context Stability: Restored sliding window attention ensures flawless attention over extended conversation histories.
Uncensored Dialogue: Dolphin alignment ablation allows fully open creative writing and unfiltered assistance.
Infinite Loop Repair: Repaired EOG tokens stop generation cleanly without repeating trailing text.

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📊 4. Benchmark Competitiveness vs. Frontier Scores

    > Evaluated Performance
BenchmarkDolphin-Mistral-24B-Venice-Edition-PatchedFrontier Target
MMLUEvaluated88.7%
GSM8KEvaluated95.6%
HumanEvalEvaluated90.2%

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🏆 5. Comprehensive Arena Analytics

> Status: Active Community Benchmarking
// Note: Arena Elo and head-to-head winrates updated continuously as evaluation telemetry processes.

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🔍 6. SWOT Analysis

> Strengths (S)
🛡️ Uncensored Fidelity: Surgically patched to ensure maximum generation throughput without alignment overhead.
⚡ Optimized Engine: Advanced mechanics ensure zero context fragmentation or execution hangs.


> Weaknesses (W)
📉 Hardware Limits: Requires sufficient VRAM/RAM for higher precision GGUF quantizations.


> Opportunities (O)
🎯 Local Sovereign Agents: Perfect for offline, private reasoning and agentic workflows.


> Threats (T)
⚠️ Sampler Sensitivity: High temperatures may require repetition penalty adjustments.

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⚡ 7. Usage & Deployment Info

> Recommended Settings
Temperature: 0.2 - 0.7
Top-P: 0.95
Backend Engines: Compatible with llama.cpp, vLLM, Ollama, LM Studio, KoboldCPP

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⚙️ 8. Backend Compatibility

> Validated Engines:
[+] llama.cpp: Native support across all quantizations.
[+] Ollama / LM Studio: Full GGUF compatibility.

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📜 9. Disclaimers & Credits

Disclaimer: Dolphin-Mistral-24B-Venice-Edition-Patched is provided for research and sovereign local deployment. As an unaligned model, users are responsible for ensuring usage complies with local laws.

Credits: Gratitude to original base model authors (dphn/Dolphin-Mistral-24B-Venice-Edition) and open-source AI community tools.

Benchmark	Dolphin-Mistral-24B-Venice-Edition-Patched	Frontier Target
MMLU	Evaluated	88.7%
GSM8K	Evaluated	95.6%
HumanEval	Evaluated	90.2%

AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched

Dolphin-Mistral-24B-Venice-Edition-Patched

> What is this model and Why is it Needed?

> From the Parent Repository

🏗️ 2. Model Architecture & Merging

🚀 3. Technical Enhancements

> Key Upgrades Over Base Model:

📊 4. Benchmark Competitiveness vs. Frontier Scores

🏆 5. Comprehensive Arena Analytics

🔍 6. SWOT Analysis

> Strengths (S)

> Weaknesses (W)

> Opportunities (O)

> Threats (T)

⚡ 7. Usage & Deployment Info

> Recommended Settings

⚙️ 8. Backend Compatibility

📜 9. Disclaimers & Credits

AIOpsInSpace/
Dolphin-Mistral-24B-Venice-Edition-Patched