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Base Eval by deval-core

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  Arxiv:2204.05149 Base model:finetune:meta-llama... Base model:meta-llama/llama-3....   Conversational   De   En   Es   Facebook   Fr   Hi   It   Llama   Llama-3   Meta   Pt   Pytorch   Region:us   Safetensors   Sharded   Tensorflow   Th
Model Card on HF 🤗: https://huggingface.co/deval-core/base-eval 

Base Eval Benchmarks

LLME Score: 0.29661
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Base Eval (deval-core/base-eval)

Base Eval Parameters and Internals

Model Type 
text generation, multilingual
Use Cases 
Areas:
Commercial, Research
Applications:
Assistant-like chat, Multilingual dialogue, Synthetic data generation
Primary Use Cases:
Instruction tuning for assistant-like chat
Limitations:
Use in unsupported languages without controls, Violations of applicable laws or the Acceptable Use Policy
Considerations:
Developers should fine-tune Llama 3.1 models for additional languages responsibly.
Additional Notes 
Developers can customize model deployment using available recipes and guidelines
Supported Languages 
en (English), de (German), fr (French), it (Italian), pt (Portuguese), hi (Hindi), es (Spanish), th (Thai)
Training Details 
Data Sources:
Publicly available online data
Data Volume:
~15 trillion tokens
Methodology:
supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
128000
Training Time:
39.3M GPU hours
Hardware Used:
H100-80GB GPUs
Model Architecture:
Auto-regressive language model using an optimized transformer architecture
Safety Evaluation 
Methodologies:
Safety fine-tuning, Red teaming
Findings:
Model must be deployed with system-level safeguards
Risk Categories:
Misinformation, Bias, Child Safety, Cybersecurity risks
Ethical Considerations:
Avoid using in unsupported languages without fine-tuning and system controls.
Responsible Ai Considerations 
Fairness:
Focus on multilingual safety and fairness across different languages
Transparency:
Clear guidelines and resources provided for deployment
Accountability:
Developers must deploy safeguards when building with the model
Mitigation Strategies:
Incorporation of safety mitigations, domain-specific evaluations
Input Output 
Input Format:
Multilingual text and multilingual text with code
Accepted Modalities:
text
Output Format:
Text, including multilingual text and code
Performance Tips:
Use transformers or llama codebase for generation
Release Notes 
Version:
3.1
Date:
2024-07-23
Notes:
Introduction of multilingual support and longer context window.
LLM NameBase Eval
Repository 🤗https://huggingface.co/deval-core/base-eval 
Base Model(s)  meta-llama/Meta-Llama-3.1-8B   meta-llama/Meta-Llama-3.1-8B
Model Size8b
Required VRAM16.1 GB
Updated2024-12-26
Maintainerdeval-core
Model Typellama
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 Languagesen de fr it pt hi es th
Model ArchitectureLlamaForCausalLM
Licensellama3.1
Context Length131072
Model Max Length131072
Transformers Version4.42.3
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than deval-core/base-eval.

Rank the Base Eval 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 v20241217
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