TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF

🤗 Hugging Face 来源apache-2.0激活 27B377 GBGGUF✓ 11 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF ./model-folder
需要做种者 →

Qwen 3.6 27B - Claude Fable 5 (Experimental)

Update · 6/22/2026
GGUFs re-done with MTP built in.
llama.cpp MTP args: --spec-type draft-mtp --spec-draft-n-max 3

Heres Qwen3.6 slightly over-trained on a very small dataset of fable 5 traces.

Had to use aggressive settings for the style transfer here due to the data contraints. Overall the model seems to be a better planner now and better at 3D modeling in three.js, making small games, and ML engineering.

Either way give it a shot, it really does look, talk, and plan like fable... but like all fine-tunes it comes with it's limitations.

Hopefully we can get some more well rounded data from the rest of the community to a do a much less aggressive tune on a larger dataset.

Reasoning was left untouched

Benchmarks

As always big thanks to @nightmedia for the speedy benchmarks.

                                 arc     arc/e	boolq
Qwen3.6-27B-Fable-5-Experimental 0.650	0.813	0.909
                     Qwen3.6-27B	0.637	0.798	0.911

Not really a benchmark but here's a procedurally generated duck that it zero-shotted lol (quant: q3_k_s)


The data for this model was easily extracted, formatted, and masked for training with Teich

This model was trained 2x faster with Unsloth and Huggingface's TRL library.