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DoppelKartoffel Mistral Nemo 23B by nbeerbower

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DoppelKartoffel Mistral Nemo 23B is an open-source language model by nbeerbower. Features: 23b LLM, VRAM: 46.3GB, Context: 1000K, License: apache-2.0, LLM Explorer Score: 0.24.

Base model:finetune:nbeerbower... Base model:nbeerbower/gigapota...   Conversational Dataset:antiven0m/physical-rea... Dataset:atsunori/helpsteer2-dp... Dataset:flammenai/date-dpo-noa... Dataset:flammenai/prude-phi3-d... Dataset:jondurbin/gutenberg-dp... Dataset:jondurbin/truthy-dpo-v... Dataset:nbeerbower/arkhaios-dp... Dataset:nbeerbower/greatfirewa... Dataset:nbeerbower/gutenberg-m... Dataset:nbeerbower/gutenberg2-...   Dataset:nbeerbower/purpura-dpo   Dataset:nbeerbower/schule-dpo   Endpoints compatible   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/nbeerbower/DoppelKartoffel-Mistral-Nemo-23B 

DoppelKartoffel Mistral Nemo 23B Benchmarks

MMLU Pro: 23.11
GPQA: 3.36
MUSR: 5.6
BBH: 31.92
IFEval: 51.91 vs 88 (so35)-41%
MATH Lvl 5: 3.1
LLME Score: 0.23608
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
DoppelKartoffel Mistral Nemo 23B (nbeerbower/DoppelKartoffel-Mistral-Nemo-23B)
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DoppelKartoffel Mistral Nemo 23B Parameters and Internals

LLM NameDoppelKartoffel Mistral Nemo 23B
Repository 🤗https://huggingface.co/nbeerbower/DoppelKartoffel-Mistral-Nemo-23B 
Base Model(s)  GigaPotato Mistral Nemo 23B   nbeerbower/GigaPotato-Mistral-Nemo-23B
Model Size23b
Required VRAM46.3 GB
Updated2026-03-13
Maintainernbeerbower
Model Typemistral
Model Files  4.9 GB: 1-of-10   4.9 GB: 2-of-10   4.9 GB: 3-of-10   4.9 GB: 4-of-10   4.9 GB: 5-of-10   4.9 GB: 6-of-10   4.9 GB: 7-of-10   4.9 GB: 8-of-10   4.9 GB: 9-of-10   2.2 GB: 10-of-10
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length1024000
Model Max Length1024000
Transformers Version4.48.1
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<pad>
Vocabulary Size131072
Torch Data Typebfloat16

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Instruction Following and Task Automation  
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