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RYS XLarge by dnhkng

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RYS XLarge is an open-source language model by dnhkng. Features: 72b LLM, VRAM: 156.3GB, Context: 32K, License: mit, Instruction-Based, LLM Explorer Score: 0.27.

  Conversational   Endpoints compatible   Instruct   Model-index   Qwen2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/dnhkng/RYS-XLarge 

RYS XLarge Benchmarks

LLME Score: 0.27483
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
RYS XLarge (dnhkng/RYS-XLarge)
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RYS XLarge Parameters and Internals

Model Type 
text-generation
Additional Notes 
This model is based on MaziyarPanahi/calme-2.1-qwen2-72b, which was tuned from Qwen2-72B.
LLM NameRYS XLarge
Repository 🤗https://huggingface.co/dnhkng/RYS-XLarge 
Model Size72b
Required VRAM156.3 GB
Updated2026-06-30
Maintainerdnhkng
Model Typeqwen2
Instruction-BasedYes
Model Files  8.8 GB: 1-of-18   8.8 GB: 2-of-18   8.8 GB: 3-of-18   8.8 GB: 4-of-18   8.8 GB: 5-of-18   8.8 GB: 6-of-18   8.8 GB: 7-of-18   8.8 GB: 8-of-18   8.8 GB: 9-of-18   8.8 GB: 10-of-18   8.8 GB: 11-of-18   8.8 GB: 12-of-18   8.8 GB: 13-of-18   8.8 GB: 14-of-18   8.8 GB: 15-of-18   8.8 GB: 16-of-18   8.8 GB: 17-of-18   6.7 GB: 18-of-18
Model ArchitectureQwen2ForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.41.0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151646
Torch Data Typebfloat16
Errorsreplace

Best Alternatives to RYS XLarge

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Context / RAM
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AceInstruct 72B128K / 146 GB13318
...n2.5 72B 2x Instruct TIES V1.0128K / 146.1 GB151
...2B PT BR Instruct Experimental128K / 146 GB60
... BI Qwen2.5 72B PT BR Instruct128K / 146 GB50
...A Abliterated TIES Qwen2.5 72B128K / 146.1 GB111
72B Qwen2.5 Kunou V1128K / 146 GB4326
EVA Qwen2.5 72B V0.1128K / 146 GB2119
EVA Qwen2.5 72B V0.0128K / 146 GB95
Qwen2 72B Instruct32K / 145.5 GB26150718
Note: green Score (e.g. "73.2") means that the model is better than dnhkng/RYS-XLarge.

Rank the RYS XLarge 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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