Infly Inf O1 Pi0 GGUF

🚀 Llamacpp imatrix Quantizations of inf-o1-pi0 by infly

This project provides quantized versions of the inf-o1-pi0 model using llama.cpp. It offers various quantization types to balance between model quality and resource usage, supporting multiple languages and easy deployment in different environments.

🚀 Quick Start

• Using LM Studio: You can run the quantized models in LM Studio.
• Using llama.cpp: Run them directly with llama.cpp, or any other llama.cpp based project.

✨ Features

• Multi - language Support: The model supports multiple languages including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.
• Diverse Quantization Options: Offers a wide range of quantization types with different file sizes and qualities, allowing users to choose according to their hardware resources and performance requirements.

📦 Installation

Download a file (not the whole branch)

Downloading using huggingface - cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/infly_inf-o1-pi0-GGUF --include "infly_inf-o1-pi0-Q4_K_M.gguf" --local-dir ./

huggingface-cli download bartowski/infly_inf-o1-pi0-GGUF --include "infly_inf-o1-pi0-Q8_0/*" --local-dir ./

💻 Usage Examples

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

📚 Documentation

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.

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 increase.

Which file should I choose?

🔧 Technical Details

• Quantization Method: Using llama.cpp release b5490 for quantization.
• Original Model: The original model is available at https://huggingface.co/infly/inf-o1-pi0.
• All quants: All quants are made using the imatrix option with a dataset from here.