bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF-torrent

The DeepSeek-Coder-V2-Lite-Instruct model, originally from deepseek-ai, has been quantized using llama.cpp release b3166. Quantization is a technique to reduce the size and computational requirements of a model without significantly impacting its performance. This repository offers a range of quantized versions of the model, catering to different needs.

Each quantized model has a specific filename, quant type, file size, and description. The quant types include Q8, Q6, Q5, Q4, Q3, and Q2, with K and L variants, as well as IQ (Int8) variants. Higher numbers indicate higher quality but larger file sizes.

To download a specific file, you can use the huggingface-cli tool. First, install it with pip install -U "huggingface_hub[cli]". Then, target the specific file you want with a command like huggingface-cli download bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF --include "DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M.gguf" --local-dir ./. For larger models split into multiple files, use a command like huggingface-cli download bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF --include "DeepSeek-Coder-V2-Lite-Instruct-Q8_0.gguf/*" --local-dir DeepSeek-Coder-V2-Lite-Instruct-Q8_0.

To choose the right file, consider your RAM and VRAM availability and the desired balance between speed, quality, and size. K-quants (like Q5_K_M) are generally recommended for ease of use, while I-quants (like IQ3_M) offer better performance for their size but are only compatible with certain architectures.

If you find this work useful, you can support the author by visiting their ko-fi page at https://ko-fi.com/bartowski.