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Platypus 2 22B Relora by chargoddard

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Platypus 2 22B Relora is an open-source language model by chargoddard. Features: 22b LLM, VRAM: 43.7GB, Context: 4K, LLM Explorer Score: 0.11, Arc: 57.7, HellaSwag: 82.4, MMLU: 55.3, GSM8K: 6.6.

Dataset:chargoddard/open-platy...   En   Endpoints compatible   Llama   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/chargoddard/platypus-2-22b-relora 

Platypus 2 22B Relora Benchmarks

ARC: 57.68 vs 96.7 (so35)-40.4%
HellaSwag: 82.44 vs 95.3 (gpt4)-13.5%
MMLU: 55.33 vs 88.3 (so35)-37.3%
GSM8K: 6.6 vs 96.4 (so35)-93.2%
LLME Score: 0.11475
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Platypus 2 22B Relora (chargoddard/platypus-2-22b-relora)
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Platypus 2 22B Relora Parameters and Internals

Model Type 
text generation
Additional Notes 
Experimental version not recommended for use yet. Mostly for testing purposes.
Training Details 
Data Sources:
Open-Platypus-Chat
Methodology:
ReLoRA training with three lora restarts for one epoch
Input Output 
Input Format:
Prefix messages with "***System:", "***Query:", or "***Response:".
LLM NamePlatypus 2 22B Relora
Repository 🤗https://huggingface.co/chargoddard/platypus-2-22b-relora 
Model Size22b
Required VRAM43.7 GB
Updated2026-05-23
Maintainerchargoddard
Model Typellama
Model Files  9.8 GB: 1-of-5   10.0 GB: 2-of-5   9.9 GB: 3-of-5   9.9 GB: 4-of-5   4.1 GB: 5-of-5
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Context Length4098
Model Max Length4098
Transformers Version4.32.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than chargoddard/platypus-2-22b-relora.

Rank the Platypus 2 22B Relora 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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