What are the hardware requirements for Dethrone R2?

Dethrone R2 requires approximately 1.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Dethrone R2 and how large is it?

Dethrone R2 is developed by Jordansky. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Dethrone R2?

Dethrone R2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Dethrone R2 by Jordansky — VRAM 1.3GB

Name: Dethrone R2
Author: Jordansky

Dethrone R2 is an open-source language model by Jordansky. Features: LLM, VRAM: 1.3GB, LLM Explorer Score: 0.24.

Arxiv:1910.09700 Base model:adapter:/cache/mode... Base model:adapter:jordansky/d... Base model:jordansky/dethrone-... Conversational Lora Peft Region:us Safetensors Sft Trl

Model Card on HF 🤗: https://huggingface.co/Jordansky/dethrone-r2

Dethrone R2 Parameters and Internals

LLM Name	Dethrone R2
Repository 🤗	https://huggingface.co/Jordansky/dethrone-r2
Base Model(s)	Jordansky/dethrone-r1 Jordansky/dethrone-r1
Required VRAM	1.3 GB
Updated	2026-07-27
Maintainer	Jordansky
Model Files	1.3 GB 0.0 GB
Model Architecture	AutoModel
Model Max Length	131072
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	v_proj\|up_proj\|q_proj\|k_proj\|down_proj\|o_proj\|gate_proj
LoRA Alpha	512
LoRA Dropout	0.1
R Param	128
Errors	replace

Rank the Dethrone R2 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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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