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Llama 3 Instruct Neurona 8B V2 by Iker

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  Autotrain compatible Base model:finetune:meta-llama... Base model:meta-llama/meta-lla...   Conversational   Dataset:csebuetnlp/crosssum Dataset:danielbrdz/barcenas-ec... Dataset:glaiveai/glaive-code-a... Dataset:glaiveai/glaive-functi...   Dataset:helsinki-nlp/opus-100   Dataset:hitz/casimedicos-exp Dataset:hitz/this-is-not-a-dat... Dataset:iker/document-translat... Dataset:iker/instructtranslati...   Dataset:iker/noticia Dataset:iker/openhermes-2.5-sp... Dataset:iker/reddit-post-trans... Dataset:projecte-aina/rag mult... Dataset:somosnlp/coser resumen... Dataset:somosnlp/es-inclusive-... Dataset:somosnlp/lenguaje-clar...   Dataset:somosnlp/lingcomp qa   Dataset:teknium/openhermes-2.5   Dataset:wikipedia   En   Endpoints compatible   Es   Instruct   Llama   Region:us   Safetensors   Sharded   Synthetic   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Iker/Llama-3-Instruct-Neurona-8b-v2 

Llama 3 Instruct Neurona 8B V2 Benchmarks

LLME Score: 0.20634
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 3 Instruct Neurona 8B V2 (Iker/Llama-3-Instruct-Neurona-8b-v2)

Llama 3 Instruct Neurona 8B V2 Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
Research, Commercial Applications
Applications:
RAG, Function Calling, Code Assistant, Question Answering, Summarization
Considerations:
Designed for experiments in improving multilingual language model infrastructure.
Additional Notes 
This is a beta version designed primarily for infrastructure and training script tuning.
Supported Languages 
es (advanced), en (advanced)
Training Details 
Data Sources:
Danielbrdz/Barcenas-Economia, HiTZ/casimedicos-exp, somosnlp/coser_resumenes, csebuetnlp/CrossSum, Iker/Document-Translation-en-es, somosnlp/es-inclusive-language-it, glaiveai/glaive-code-assistant-v3, glaiveai/glaive-function-calling-v2, Iker/InstructTranslation-EN-ES, somosnlp/lenguaje-claro-dataset, somosnlp/LingComp_QA, Iker/NoticIA, teknium/OpenHermes-2.5, Iker/OpenHermes-2.5-Spanish, Helsinki-NLP/opus-100, projecte-aina/RAG_Multilingual, HiTZ/This-is-not-a-dataset, wikipedia, Iker/Reddit-Post-Translation
Methodology:
Llama-based fine-tuning with a diverse set of datasets across Spanish and English languages to enhance multilingual abilities.
Context Length:
8192
Hardware Used:
4x Nvidia A100 80GB
Model Architecture:
AutoModelForCausalLM
Input Output 
Input Format:
LLM prompts in Spanish/English
Accepted Modalities:
text
Output Format:
Text generation
Performance Tips:
Leverage bilingual capabilities for best results.
LLM NameLlama 3 Instruct Neurona 8B V2
Repository 🤗https://huggingface.co/Iker/Llama-3-Instruct-Neurona-8b-v2 
Base Model(s)  Meta Llama 3 8B Instruct   meta-llama/Meta-Llama-3-8B-Instruct
Model Size8b
Required VRAM16.1 GB
Updated2025-01-20
MaintainerIker
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   1.2 GB: 4-of-4
Supported Languageses en
Model ArchitectureLlamaForCausalLM
Licensellama3
Context Length8192
Model Max Length8192
Transformers Version4.42.2
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size128264
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Iker/Llama-3-Instruct-Neurona-8b-v2.

Rank the Llama 3 Instruct Neurona 8B V2 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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Release v20241227
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