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llama-13b.ggmlv3.q2_K.bin
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q2_K
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2
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5.43 GB
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7.93 GB
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New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
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llama-13b.ggmlv3.q3_K_L.bin
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q3_K_L
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3
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6.87 GB
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9.37 GB
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New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
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llama-13b.ggmlv3.q3_K_M.bin
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q3_K_M
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3
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6.25 GB
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8.75 GB
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New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
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llama-13b.ggmlv3.q3_K_S.bin
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q3_K_S
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3
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5.59 GB
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8.09 GB
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New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
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llama-13b.ggmlv3.q4_0.bin
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q4_0
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4
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7.32 GB
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9.82 GB
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Original quant method, 4-bit.
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llama-13b.ggmlv3.q4_1.bin
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q4_1
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4
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8.14 GB
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10.64 GB
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Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
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llama-13b.ggmlv3.q4_K_M.bin
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q4_K_M
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4
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7.82 GB
|
10.32 GB
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New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
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llama-13b.ggmlv3.q4_K_S.bin
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q4_K_S
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4
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7.32 GB
|
9.82 GB
|
New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
|
|
llama-13b.ggmlv3.q5_0.bin
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q5_0
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5
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8.95 GB
|
11.45 GB
|
Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
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llama-13b.ggmlv3.q5_1.bin
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q5_1
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5
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9.76 GB
|
12.26 GB
|
Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
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|
llama-13b.ggmlv3.q5_K_M.bin
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q5_K_M
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5
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9.21 GB
|
11.71 GB
|
New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
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|
llama-13b.ggmlv3.q5_K_S.bin
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q5_K_S
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5
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8.95 GB
|
11.45 GB
|
New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
|
|
llama-13b.ggmlv3.q6_K.bin
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q6_K
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6
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10.68 GB
|
13.18 GB
|
New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization
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|
llama-13b.ggmlv3.q8_0.bin
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q8_0
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8
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13.83 GB
|
16.33 GB
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Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
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