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Mistral Nemo Base 2407 by mistralai

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Model Card on HF 🤗: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407 

Mistral Nemo Base 2407 Benchmarks

MMLU Pro: 27.46
GPQA: 5.82
MUSR: 6.52
BBH: 29.37
IFEval: 16.3 vs 88 (so35)-81.5%
MATH Lvl 5: 5.97
LLME Score: 0.29705
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mistral Nemo Base 2407 (mistralai/Mistral-Nemo-Base-2407)
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Mistral Nemo Base 2407 Parameters and Internals

Model Type 
generative text
Use Cases 
Areas:
research, commercial applications
Additional Notes 
Mistral-Nemo-Base-2407 is a pretrained base model and does not have moderation mechanisms.
Supported Languages 
en (English), fr (French), de (German), es (Spanish), it (Italian), pt (Portuguese), ru (Russian), zh (Chinese), ja (Japanese)
Training Details 
Data Sources:
multilingual and code data
Methodology:
pretrained base
Context Length:
128000
Model Architecture:
transformer
Input Output 
Accepted Modalities:
text
Performance Tips:
Use a temperature of 0.3 for optimal performance.
LLM NameMistral Nemo Base 2407
Repository 🤗https://huggingface.co/mistralai/Mistral-Nemo-Base-2407 
Model Size12.2b
Required VRAM24.5 GB
Updated2025-10-17
Maintainermistralai
Model Files  24.5 GB   4.9 GB: 1-of-5   4.9 GB: 2-of-5   4.9 GB: 3-of-5   4.9 GB: 4-of-5   4.9 GB: 5-of-5
Supported Languagesen fr de es it pt ru zh ja
Model ArchitectureMambaSSM
LicenseMNPL-0.1
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size131072

Quantized Models of the Mistral Nemo Base 2407

Model
Likes
Downloads
VRAM
Normistral 11B Warm928222 GB

Best Alternatives to Mistral Nemo Base 2407

Best Alternatives
Context / RAM
Downloads
Likes
Mistral Nemo Instruct 24070K / 24.5 GB869631598
MistralAI Nemo Instruct ChatML0K / 24.5 GB2722
...Nemo Instruct 2407 Abliterated0K / 24.5 GB913
Note: green Score (e.g. "73.2") means that the model is better than mistralai/Mistral-Nemo-Base-2407.

Rank the Mistral Nemo Base 2407 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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