README.md

metadata

base_model: mistralai/Mistral-Large-Instruct-2407
language:
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license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
pipeline_tag: text-generation
quantized_by: bartowski
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Llamacpp imatrix Quantizations of Mistral-Large-Instruct-2407

Using llama.cpp release b3441 for quantization.

Original model: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407

All quants made using imatrix option with dataset from here

Run them in LM Studio

Prompt format

No chat template specified so default is used. This may be incorrect, check original model card for details.

<s>[INST] <<SYS>>
{system_prompt}
<</SYS>>

{prompt} [/INST]  </s>

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Split	Description
Mistral-Large-Instruct-2407-Q4_K_M.gguf	Q4_K_M	73.22GB	true	Good quality, default size for must use cases, recommended.
Mistral-Large-Instruct-2407-IQ4_XS.gguf	IQ4_XS	65.43GB	true	Decent quality, smaller than Q4_K_S with similar performance, recommended.
Mistral-Large-Instruct-2407-Q3_K_M.gguf	Q3_K_M	59.10GB	true	Low quality.
Mistral-Large-Instruct-2407-IQ3_M.gguf	IQ3_M	55.28GB	true	Medium-low quality, new method with decent performance comparable to Q3_K_M.
Mistral-Large-Instruct-2407-Q3_K_S.gguf	Q3_K_S	52.85GB	true	Low quality, not recommended.
Mistral-Large-Instruct-2407-IQ3_XXS.gguf	IQ3_XXS	47.01GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
Mistral-Large-Instruct-2407-Q2_K.gguf	Q2_K	45.20GB	false	Very low quality but surprisingly usable.
Mistral-Large-Instruct-2407-IQ2_M.gguf	IQ2_M	41.62GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.
Mistral-Large-Instruct-2407-IQ2_XS.gguf	IQ2_XS	36.08GB	false	Low quality, uses SOTA techniques to be usable.
Mistral-Large-Instruct-2407-IQ2_XXS.gguf	IQ2_XXS	32.43GB	false	Very low quality, uses SOTA techniques to be usable.
Mistral-Large-Instruct-2407-IQ1_M.gguf	IQ1_M	28.39GB	false	Extremely low quality, not recommended.

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset

Thank you ZeroWw for the inspiration to experiment with embed/output

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/Mistral-Large-Instruct-2407-GGUF --include "Mistral-Large-Instruct-2407-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/Mistral-Large-Instruct-2407-GGUF --include "Mistral-Large-Instruct-2407-Q8_0.gguf/*" --local-dir Mistral-Large-Instruct-2407-Q8_0

You can either specify a new local-dir (Mistral-Large-Instruct-2407-Q8_0) or download them all in place (./)

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