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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


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


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


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...


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 }
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