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mlabonne/gemma-2b-GGUF
gemma-2b-GGUF is a machine learning model from mlabonne. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
This is a quantized version of the google/gemma-2b model using llama.cpp.
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From the Hugging Face model README
This is a quantized version of the google/gemma-2b model using llama.cpp.
This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the 7B base model, 7B instruct model, and 2B instruct model.
Model Page: Gemma
Terms of Use: Terms
q2_k: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.q3_k_l: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_Kq3_k_m: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_Kq3_k_s: Uses Q3_K for all tensorsq4_0: Original quant method, 4-bit.q4_1: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.q4_k_m: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_Kq4_k_s: Uses Q4_K for all tensorsq5_0: Higher accuracy, higher resource usage and slower inference.q5_1: Even higher accuracy, resource usage and slower inference.q5_k_m: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_Kq5_k_s: Uses Q5_K for all tensorsq6_k: Uses Q8_K for all tensorsq8_0: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.This model can be used with the latest version of llama.cpp and LM Studio >0.2.16.