Original model: https://huggingface.co/google/codegemma-7b-it All quants made using imatrix option with dataset provided by Kalomaze here 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 ‘QXKX’, like Q5KM. If you want to get more into the weeds, you can check out this extremely useful feature chart: 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 IQXX, like IQ3M. These are newer and offer better…
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Генерация текста
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Диалог / чат
Задача: Генерация текста
Автор: bartowski
Теги: gguf, endpoints_compatible, conversational
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Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
