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| Name | Quant method | Size |
|---|---|---|
| gpt2_1558M_final4_hf.Q2_K.gguf | Q2_K | 0.84GB |
| gpt2_1558M_final4_hf.IQ3_XS.gguf | IQ3_XS | 0.84GB |
| gpt2_1558M_final4_hf.IQ3_S.gguf | IQ3_S | 0.84GB |
| gpt2_1558M_final4_hf.Q3_K_S.gguf | Q3_K_S | 0.84GB |
| gpt2_1558M_final4_hf.IQ3_M.gguf | IQ3_M | 0.91GB |
| gpt2_1558M_final4_hf.Q3_K.gguf | Q3_K | 0.97GB |
| gpt2_1558M_final4_hf.Q3_K_M.gguf | Q3_K_M | 0.97GB |
| gpt2_1558M_final4_hf.Q3_K_L.gguf | Q3_K_L | 1.03GB |
| gpt2_1558M_final4_hf.IQ4_XS.gguf | IQ4_XS | 0.9GB |
| gpt2_1558M_final4_hf.Q4_0.gguf | Q4_0 | 0.91GB |
| gpt2_1558M_final4_hf.IQ4_NL.gguf | IQ4_NL | 0.91GB |
| gpt2_1558M_final4_hf.Q4_K_S.gguf | Q4_K_S | 1.04GB |
| gpt2_1558M_final4_hf.Q4_K.gguf | Q4_K | 1.11GB |
| gpt2_1558M_final4_hf.Q4_K_M.gguf | Q4_K_M | 1.11GB |
| gpt2_1558M_final4_hf.Q4_1.gguf | Q4_1 | 1.0GB |
| gpt2_1558M_final4_hf.Q5_0.gguf | Q5_0 | 1.09GB |
| gpt2_1558M_final4_hf.Q5_K_S.gguf | Q5_K_S | 1.15GB |
| gpt2_1558M_final4_hf.Q5_K.gguf | Q5_K | 1.29GB |
| gpt2_1558M_final4_hf.Q5_K_M.gguf | Q5_K_M | 1.29GB |
| gpt2_1558M_final4_hf.Q5_1.gguf | Q5_1 | 1.18GB |
| gpt2_1558M_final4_hf.Q6_K.gguf | Q6_K | 1.52GB |
| gpt2_1558M_final4_hf.Q8_0.gguf | Q8_0 | 1.63GB |
-x 400000), but it became unstable later in training and exploded around step 330K. Because I was losing my computing quota shortly, I decided to just rewind back to checkpoint 300K, and then instead of going all the way to 400K I started annealing linearly down to 330K. This went without incident and produced this model.