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example$ python3 ./make-ggml.py --model /home/inpw/Pygmalion-1.1-7b --outname Pygmalion-Vicuna-1.1-7b --outdir /home/inpw/Pygmalion-Vicuna-1.1-7b --keep_fp16 --quants ...USE_POLICY.md making sure to comply with license agreements / legalities.| Quant Method | Use Case |
|---|---|
| Q2_K | 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. |
| Q3_K_S | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| Q3_K_M | 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 |
| Q3_K_L | 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 |
| Q4_0 | Original quant method, 4-bit. |
| Q4_1 | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| Q4_K_S | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| Q4_K_M | 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 |
| Q5_0 | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| Q5_1 | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| Q5_K_S | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| Q5_K_M | 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 |
| Q6_K | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| fp16 | Compiled Safetensors, can be used to quantize |
| RAM/VRAM | Parameters | GPU Offload (2K ctx, Q4_0, 6GB RTX 2060) |
|---|---|---|
| 4GB | 3B | |
| 8GB | 7B | 32 Layers |
| 16GB | 13B | 18 Layers |
| 32GB | 30B | 8 Layers |
| 64GB | 65B |