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| Name | Quant method | Size |
|---|---|---|
| Yi-Ko-6B-Instruct-v1.0.Q2_K.gguf | Q2_K | 2.24GB |
| Yi-Ko-6B-Instruct-v1.0.IQ3_XS.gguf | IQ3_XS | 2.48GB |
| Yi-Ko-6B-Instruct-v1.0.IQ3_S.gguf | IQ3_S | 2.6GB |
| Yi-Ko-6B-Instruct-v1.0.Q3_K_S.gguf | Q3_K_S | 2.59GB |
| Yi-Ko-6B-Instruct-v1.0.IQ3_M.gguf | IQ3_M | 2.69GB |
| Yi-Ko-6B-Instruct-v1.0.Q3_K.gguf | Q3_K | 2.86GB |
| Yi-Ko-6B-Instruct-v1.0.Q3_K_M.gguf | Q3_K_M | 2.86GB |
| Yi-Ko-6B-Instruct-v1.0.Q3_K_L.gguf | Q3_K_L | 3.08GB |
| Yi-Ko-6B-Instruct-v1.0.IQ4_XS.gguf | IQ4_XS | 3.18GB |
| Yi-Ko-6B-Instruct-v1.0.Q4_0.gguf | Q4_0 | 3.32GB |
| Yi-Ko-6B-Instruct-v1.0.IQ4_NL.gguf | IQ4_NL | 3.35GB |
| Yi-Ko-6B-Instruct-v1.0.Q4_K_S.gguf | Q4_K_S | 3.34GB |
| Yi-Ko-6B-Instruct-v1.0.Q4_K.gguf | Q4_K | 3.5GB |
| Yi-Ko-6B-Instruct-v1.0.Q4_K_M.gguf | Q4_K_M | 3.5GB |
| Yi-Ko-6B-Instruct-v1.0.Q4_1.gguf | Q4_1 | 3.66GB |
| Yi-Ko-6B-Instruct-v1.0.Q5_0.gguf | Q5_0 | 4.0GB |
| Yi-Ko-6B-Instruct-v1.0.Q5_K_S.gguf | Q5_K_S | 4.0GB |
| Yi-Ko-6B-Instruct-v1.0.Q5_K.gguf | Q5_K | 4.09GB |
| Yi-Ko-6B-Instruct-v1.0.Q5_K_M.gguf | Q5_K_M | 4.09GB |
| Yi-Ko-6B-Instruct-v1.0.Q5_1.gguf | Q5_1 | 4.34GB |
| Yi-Ko-6B-Instruct-v1.0.Q6_K.gguf | Q6_K | 4.72GB |
| Yi-Ko-6B-Instruct-v1.0.Q8_0.gguf | Q8_0 | 6.12GB |
| Model | kobest_boolq | kobest_copa | kobest_hellaswag | kobest_sentineg | korunsmile | pawsx_ko |
|---|---|---|---|---|---|---|
| Zero-shot | ||||||
| Yi-Ko-6B-Instruct-v1.0 | 0.6619 | 0.7794 | 0.4858 | 0.4589 | 0.3520 | 0.5545 |
| Yi-Ko-6B | 0.7070 | 0.7696 | 0.5009 | 0.4044 | 0.3828 | 0.5145 |
1### User:
2{instruction}
3
4### Assistant:
5{response}1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4tokenizer = AutoTokenizer.from_pretrained("wkshin89/Yi-Ko-6B-Instruct-v1.0")
5model = AutoModelForCausalLM.from_pretrained(
6 "wkshin89/Yi-Ko-6B-Instruct-v1.0",
7 device_map="auto",
8 torch_dtype=torch.bfloat16,
9)