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GGUF quantized version of OpenMath2-Llama3.1-8B

project original source (finetuned model)

Q_2_K (not nice)

Q_3_K_S (acceptable)

Q_3_K_M is acceptable (good for running with CPU)

Q_3_K_L (acceptable)

Q_4_K_S (okay)

Q_4_K_M is recommanded (balance)

Q_5_K_S (good)

Q_5_K_M (good in general)

Q_6_K is good also; if you want a better result; take this one instead of Q_5_K_M

Q_8_0 which is very good; need a reasonable size of RAM otherwise you might expect a long wait

f16 is similar to the original hf model; opt this one or hf also fine; make sure you have a good machine

*the latest update includes Q_4_0, Q_4_1 (belong to Q4 family) and Q_5_0, Q_5_1 (Q5 family)

how to run it

use any connector for interacting with gguf; i.e., gguf-connector

the chart and figure above are from finetuned model (nvidia side); those are used for comparing between the finetuned model and the base model; and the base model is from meta

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GGUF
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Architecture
llama

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