LLM EXPLORER 59,358 MODELS INDEXED

Base Eval by deval-core

By deval-core · 15 downloads
HF Repo ↗ Copy URL

Base Eval is an open-source language model by deval-core. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, License: llama3.1, LLM Explorer Score: 0.15.

  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 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
Updated2026-08-08
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

Best Alternatives to Base Eval

Best Alternatives
Context / RAM
Downloads
Likes
...otron 8B UltraLong 4M Instruct4192K / 32.1 GB1135125
UltraLong Thinking4192K / 16.1 GB23
...a 3.1 8B UltraLong 4M Instruct4192K / 32.1 GB17624
...a 3.1 8B UltraLong 2M Instruct2096K / 32.1 GB8759
...otron 8B UltraLong 2M Instruct2096K / 32.1 GB12418
Cthulhu 8B V1.41048K / 16.1 GB1010
...raLong 1M Instruct Abliterated1048K / 32.1 GB49
...a 3.1 8B UltraLong 1M Instruct1048K / 32.1 GB138729
...otron 8B UltraLong 1M Instruct1048K / 32.1 GB70259
...xis Bookwriter Llama3.1 8B Sft1048K / 16.1 GB254
Note: green Score (e.g. "73.2") means that the model is better than deval-core/base-eval.