Light tune on Qwen3.8 on some the datasets cited here, as well as a large corpus of personal (private) Fable 5 data
| Model | ARC Challenge | ARC Challenge (Easy) | BoolQ |
|---|---|---|---|
| Qwen3.8-27B | 0.591 | 0.782 | 0.896 |
| Qwen3.8-27B-Fable-Distill | 0.637 | 0.832 | 0.911 |
As always, big thank you to @nightmedia for the benchmarks
The data for this model was easily formatted, validated, and masked using Teich
This qwen3_5 model was trained 2x faster with Unsloth and Huggingface's TRL library.
model-00004-of-00018.safetensors 4.0 GBmodel-00016-of-00018.safetensors 4.0 GBmodel-00006-of-00018.safetensors 4.0 GBmodel-00008-of-00018.safetensors 4.0 GBmodel-00010-of-00018.safetensors 4.0 GBmodel-00012-of-00018.safetensors 4.0 GBmodel-00014-of-00018.safetensors 4.0 GBmodel-00001-of-00018.safetensors 4.0 GBmodel-00018-of-00018.safetensors 3.4 GBmodel-00002-of-00018.safetensors 3.0 GBmodel-00003-of-00018.safetensors 2.5 GBmodel-00007-of-00018.safetensors 2.1 GBmodel-00009-of-00018.safetensors 2.1 GBmodel-00011-of-00018.safetensors 2.1 GBmodel-00013-of-00018.safetensors 2.1 GBmodel-00015-of-00018.safetensors 2.1 GBmodel-00017-of-00018.safetensors 2.1 GBmodel-00005-of-00018.safetensors 2.1 GB有文件?提交种子。
提交种子# Remote endpoint: http://sense-gpu.senseaition.rocks:13305 servedModel: Qwen3.8-27B-Fable-Distill-GGUF-Q4_K_M在 LocalMaxxing 上查看测试
来自 LocalMaxxing 的 3 次社区测试(1 种配置)的中位数。
自己运行:配置指南