Turdus-7B-GGUF

Description

This repo contains GGUF format model files for Turdus-7B-GGUF.

Files Provided

Name	Quant	Bits	File Size	Remark
turdus-7b.IQ3_XXS.gguf	IQ3_XXS	3	3.02 GB	3.06 bpw quantization
turdus-7b.IQ3_S.gguf	IQ3_S	3	3.18 GB	3.44 bpw quantization
turdus-7b.IQ3_M.gguf	IQ3_M	3	3.28 GB	3.66 bpw quantization mix
turdus-7b.Q4_0.gguf	Q4_0	4	4.11 GB	3.56G, +0.2166 ppl
turdus-7b.IQ4_NL.gguf	IQ4_NL	4	4.16 GB	4.25 bpw non-linear quantization
turdus-7b.Q4_K_M.gguf	Q4_K_M	4	4.37 GB	3.80G, +0.0532 ppl
turdus-7b.Q5_K_M.gguf	Q5_K_M	5	5.13 GB	4.45G, +0.0122 ppl
turdus-7b.Q6_K.gguf	Q6_K	6	5.94 GB	5.15G, +0.0008 ppl
turdus-7b.Q8_0.gguf	Q8_0	8	7.70 GB	6.70G, +0.0004 ppl

Parameters

path	type	architecture	rope_theta	sliding_win	max_pos_embed
udkai/Turdus	mistral	MistralForCausalLM	10000.0	4096	32768

Benchmarks

Specific Purpose Notes

This model understands classification very well. Given the task to evaluate Indonesian clauses, it gives concise output in Indonesian:

Even better in English (with slight different prompt):

Excellent clause classification for evaluation preparation:

Original Model Card

udkai_Turdus

A less contaminated version of udkai/Garrulus and the second model to be discussed in the paper Subtle DPO-Contamination with modified Winogrande increases TruthfulQA, Hellaswag & ARC.

Contrary to Garrulus which was obtained after 2 epochs, this model was obtained after one single epoch of "direct preference optimization" of NeuralMarcoro14-7B with [https://huggingface.co/datasets/hromi/winograd_dpo ] .

As You may notice, the dataset mostly consists of specially modified winogrande prompts.

But before flagging this (or recommending this to be flagged), consider this:

Subtle DPO-Contamination with modified Winogrande causes the average accuracy of all 5-non Winogrande metrics (e.g. including also MMLU and GSM8K) to be 0.2% higher than the underlying model.

Model	ARC	HellaSwag	MMLU	Truthful QA	GSM8K	Average
mlabonne/NeuralMarcoro14-7B	71.42	87.59	64.84	65.64	70.74	72.046
udkai/Turdus	73.38	88.56	64.52	67.11	67.7	72,254

Yes, as strange as it may sound, one can indeed increase ARC from 71.42% to 73.38 % with one single epoch of cca 1200 repetitive winograd schematas...

BibTex

Should this model - or quasi-methodology which lead to it - be of certain pratical or theoretical interest for You, would be honored if You would refer to it in Your work:

@misc {udk_dot_ai_turdus,
    author       = { {UDK dot AI, Daniel Devatman Hromada} },
    title        = { Turdus (Revision 923c305) },
    year         = 2024,
    url          = { https://huggingface.co/udkai/Turdus },
    doi          = { 10.57967/hf/1611 },
    publisher    = { Hugging Face }
}