SauerkrautLM Gemma 7B by VAGOsolutions

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  Alpha   Autotrain compatible   De   Dpo   En   Endpoints compatible   Finetuned   Gemma   Laser-qlora   Laserrmt   Region:us   Safetensors   Sft   Sharded   Tensorflow   Work in progress

SauerkrautLM Gemma 7B Benchmarks

SauerkrautLM Gemma 7B (VAGOsolutions/SauerkrautLM-Gemma-7b)

SauerkrautLM Gemma 7B Parameters and Internals

Model Type 
finetuned
Additional Notes 
Early stage finetuned model with potential strange behavior.
Supported Languages 
de (trained), en (trained)
Training Details 
Data Sources:
SFT, DPO, Laser data(x), Laser again on data(y)
Methodology:
Spherical Linear Interpolation and a lasered version of the model, partially freezing with laser-like analysis
Model Architecture:
novel training technique: laser-QLoRA
Input Output 
Input Format:
vicuna prompt template
Performance Tips:
Use stopping strings "~~", " "
Release Notes 
Version:
01.03.2024
Date:
2024-03-01
Notes:
Reuploaded the model in bfloat16 dtype.
Version:
02.03.2024
Date:
2024-03-02
Notes:
Strongest Gemma finetune model so far with additional scoring.
LLM NameSauerkrautLM Gemma 7B
Repository ๐Ÿค—https://huggingface.co/VAGOsolutions/SauerkrautLM-Gemma-7b 
Model Size7b
Required VRAM17.1 GB
Updated2024-12-21
MaintainerVAGOsolutions
Model Typegemma
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   2.1 GB: 4-of-4
Supported Languagesde en
Model ArchitectureGemmaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.39.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than VAGOsolutions/SauerkrautLM-Gemma-7b.

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217