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CroissantLLMBase by croissantllm

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CroissantLLMBase is an open-source language model by croissantllm. Features: 627b LLM, VRAM: 5.4GB, Context: 2K, License: mit, LLM Explorer Score: 0.17, Arc: 30.6, HellaSwag: 54.2, MMLU: 25.7, GSM8K: 0.6.

  Arxiv:2402.00786   Art   Code   Dataset:bigcode/starcoderdata Dataset:cerebras/slimpajama-62... Dataset:croissantllm/croissant...   Dataset:pg19   Dataset:uonlp/culturax   Deploy:azure   En   Endpoints compatible   Fr   Legal   Llama   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/croissantllm/CroissantLLMBase 

CroissantLLMBase Benchmarks

ARC: 30.63 vs 96.7 (so35)-68.3%
HellaSwag: 54.18 vs 95.3 (gpt4)-43.1%
MMLU: 25.72 vs 88.3 (so35)-70.9%
GSM8K: 0.61 vs 96.4 (so35)-99.4%
LLME Score: 0.16912
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CroissantLLMBase (croissantllm/CroissantLLMBase)
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CroissantLLMBase Parameters and Internals

Model Type 
text generation
Additional Notes 
The final model is suited for few-shot prompting strategies and base functions, not finetuned for chat capabilities.
Supported Languages 
en (proficient), fr (proficient)
Training Details 
Data Sources:
cerebras/SlimPajama-627B, uonlp/CulturaX, pg19, bigcode/starcoderdata, croissantllm/croissant_dataset
Data Volume:
3T tokens in English and French
Methodology:
Bilingual training with a custom tokenizer and bilingual finetuning datasets
LLM NameCroissantLLMBase
Repository 🤗https://huggingface.co/croissantllm/CroissantLLMBase 
Model Size627b
Required VRAM5.4 GB
Updated2026-05-12
Maintainercroissantllm
Model Typellama
Model Files  5.0 GB: 1-of-2   0.4 GB: 2-of-2
Supported Languagesfr en
Model ArchitectureLlamaForCausalLM
Licensemit
Context Length2048
Model Max Length2048
Transformers Version4.35.0.dev0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than croissantllm/CroissantLLMBase.

Rank the CroissantLLMBase Capabilities

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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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