Nomic Ai Nomic Embed Code GGUF

🚀 Llamacpp imatrix Quantizations of nomic-embed-code by nomic-ai

This project provides Llama.cpp imatrix quantizations of the nomic-embed-code model from Nomic AI. It uses specific Llama.cpp releases for quantization and offers various quantized models with different characteristics for text generation tasks.

🚀 Quick Start

• Using llama.cpp: Use llama.cpp release b5284 for quantization.
• Running in LM Studio: You can run the quantized models in LM Studio.
• Running with llama.cpp: Run them directly with llama.cpp, or any other llama.cpp based project.

✨ Features

• Multiple Quantization Types: Offers a wide range of quantized models with different quantization types (e.g., bf16, Q8_0, Q6_K_L, etc.) to suit various performance and quality requirements.
• Specific Prompt Format: Defines a specific prompt format for interacting with the models.
• Download Options: Provides instructions on downloading specific files using the huggingface-cli.

📚 Documentation

Prompt format

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

Download a file (not the whole branch)

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.

Downloading using huggingface-cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/nomic-ai_nomic-embed-code-GGUF --include "nomic-ai_nomic-embed-code-Q4_K_M.gguf" --local-dir ./

huggingface-cli download bartowski/nomic-ai_nomic-embed-code-GGUF --include "nomic-ai_nomic-embed-code-Q8_0/*" --local-dir ./

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.

Which file should I choose?

📄 License

This project is licensed under the Apache-2.0 license.