Cognitivecomputations Dolphin Mistral 24B Venice Edition GGUF

🚀 Llamacpp imatrix Quantizations of Dolphin-Mistral-24B-Venice-Edition by cognitivecomputations

This project provides quantized versions of the Dolphin-Mistral-24B-Venice-Edition model using llama.cpp, offering various quantization types for different performance and quality requirements.

📋 Metadata

🚀 Quick Start

• Quantization Method: Using llama.cpp release b5317 for quantization.
• Original Model: https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
• Quantization Dataset: All quants made using imatrix option with dataset from here
• Running the Model:
  • Run them in LM Studio
  • Run them directly with llama.cpp, or any other llama.cpp based project

✨ Features

Prompt Format

<s>[SYSTEM_PROMPT]{system_prompt}[/SYSTEM_PROMPT][INST]{prompt}[/INST]

Download Options

You can download a specific file from the following table:

Embed/Output Weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

📦 Installation

Downloading using huggingface-cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-GGUF --include "cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-Q4_K_M.gguf" --local-dir ./

huggingface-cli download bartowski/cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-GGUF --include "cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-Q8_0/*" --local-dir ./

🔧 Technical Details

ARM/AVX Information

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

📄 License

This project is licensed under the apache-2.0 license.