Instructions to use AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M

Use Docker

docker model run hf.co/AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M

LM Studio
Jan

vLLM

How to use AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M

Ollama
How to use AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched with Ollama:
```
ollama run hf.co/AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M
```

Unsloth Studio

How to use AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched to start chatting

Atomic Chat new
Docker Model Runner
How to use AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched with Docker Model Runner:
```
docker model run hf.co/AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M
```

Lemonade

How to use AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched:Q4_K_M

Run and chat with the model

lemonade run user.Dolphin-Mistral-24B-Venice-Edition-Patched-Q4_K_M

List all available models

lemonade list

"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

↑ Back to Top

🏗️ 2. Model Architecture & Merging

Architecture: Mistral 24B Transformer Architecture with Sliding Window Attention

Merging Technique: Attention Parameter Restoration & Tokenizer Patching

Constituent Models:
Base Model: dphn/Dolphin-Mistral-24B-Venice-Edition

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

↑ Back to Top

🚀 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.

↑ Back to Top

📊 4. Benchmark Competitiveness vs. Frontier Scores

    > Evaluated Performance



Benchmark
Dolphin-Mistral-24B-Venice-Edition-Patched
Frontier Target


MMLU
Evaluated
88.7%

GSM8K
Evaluated
95.6%

HumanEval
Evaluated
90.2%

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

↑ Back to Top

🏆 5. Comprehensive Arena Analytics

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

↑ Back to Top

🔍 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.

↑ Back to Top

⚡ 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

↑ Back to Top

⚙️ 8. Backend Compatibility

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

↑ Back to Top

📜 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.

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GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for AIOpsInSpace/Dolphin-Mistral-24B-Venice-Edition-Patched

Base model

mistralai/Mistral-Small-24B-Base-2501

Finetuned

mistralai/Mistral-Small-24B-Instruct-2501

Finetuned

dphn/Dolphin-Mistral-24B-Venice-Edition

Quantized

(53)

this model