E N V Y Legion V2.1 LLaMa 70B Elarablated V0.8 Hf GGUF

🚀 Llamacpp imatrix Quantizations of Legion-V2.1-LLaMa-70B-Elarablated-v0.8-hf by e-n-v-y

This project offers quantized versions of the Legion-V2.1-LLaMa-70B-Elarablated-v0.8-hf model. It provides various quantization types to meet different performance and storage requirements, enabling users to run the model efficiently on different platforms.

🚀 Quick Start

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

✨ Features

• Multiple Quantization Types: Offers a wide range of quantization types, such as Q8_0, Q6_K, Q5_K_M, etc., to balance between model quality and file size.
• Online Repacking: Some quantization types support online repacking, which can automatically optimize weights for better performance on ARM and AVX machines.

📦 Installation

Prerequisites

Make sure you have huggingface-cli installed:

pip install -U "huggingface_hub[cli]"

Download a Single File

huggingface-cli download bartowski/e-n-v-y_Legion-V2.1-LLaMa-70B-Elarablated-v0.8-hf-GGUF --include "e-n-v-y_Legion-V2.1-LLaMa-70B-Elarablated-v0.8-hf-Q4_K_M.gguf" --local-dir ./

Download Split Files

If the model is bigger than 50GB and split into multiple files, run:

huggingface-cli download bartowski/e-n-v-y_Legion-V2.1-LLaMa-70B-Elarablated-v0.8-hf-GGUF --include "e-n-v-y_Legion-V2.1-LLaMa-70B-Elarablated-v0.8-hf-Q8_0/*" --local-dir ./

💻 Usage Examples

Prompt Format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|>
<|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>

📚 Documentation

Download a File

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 incrase.