bartowski/zephyr-orpo-141b-A35b-v0.1-GGUF

Llamacpp Quantizations of zephyr-orpo-141b-A35b-v0.1

Using llama.cpp release b2636 for quantization.

Original model: https://huggingface.co./HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1

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) from below:

This is a very large model and has been split into parts to upload. Recent versions of llamacpp allow you to load part 1 and the rest will load as well. Make sure you download all parts of the chosen size.

Filename	Quant type	File Size	Description
zephyr-orpo-141b-A35b-v0.1-Q5_K_M.gguf	Q5_K_M	99.96GB	High quality, recommended.
zephyr-orpo-141b-A35b-v0.1-Q5_K_S.gguf	Q5_K_S	96.97GB	High quality, recommended.
zephyr-orpo-141b-A35b-v0.1-Q4_K_M.gguf	Q4_K_M	85.58GB	Good quality, uses about 4.83 bits per weight, recommended.
zephyr-orpo-141b-A35b-v0.1-Q4_K_S.gguf	Q4_K_S	80.47GB	Slightly lower quality with more space savings, recommended.
zephyr-orpo-141b-A35b-v0.1-IQ4_NL.gguf	IQ4_NL	80.47GB	Decent quality, slightly smaller than Q4_K_S with similar performance recommended.
zephyr-orpo-141b-A35b-v0.1-IQ4_XS.gguf	IQ4_XS	76.35GB	Decent quality, smaller than Q4_K_S with similar performance, recommended.
zephyr-orpo-141b-A35b-v0.1-Q3_K_L.gguf	Q3_K_L	72.57GB	Lower quality but usable, good for low RAM availability.
zephyr-orpo-141b-A35b-v0.1-Q3_K_M.gguf	Q3_K_M	67.78GB	Even lower quality.
zephyr-orpo-141b-A35b-v0.1-IQ3_M.gguf	IQ3_M	64.49GB	Medium-low quality, new method with decent performance comparable to Q3_K_M.
zephyr-orpo-141b-A35b-v0.1-IQ3_S.gguf	IQ3_S	61.49GB	Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance.
zephyr-orpo-141b-A35b-v0.1-Q3_K_S.gguf	Q3_K_S	61.49GB	Low quality, not recommended.
zephyr-orpo-141b-A35b-v0.1-IQ3_XS.gguf	IQ3_XS	58.22GB	Lower quality, new method with decent performance, slightly better than Q3_K_S.
zephyr-orpo-141b-A35b-v0.1-Q2_K.gguf	Q2_K	52.10GB	Very low quality but surprisingly usable.

Which file should I choose?

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski