What are the hardware requirements for Vicuna 13B Self Consistency Random Var 5?

Vicuna 13B Self Consistency Random Var 5 requires approximately 26 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Vicuna 13B Self Consistency Random Var 5 and how large is it?

Vicuna 13B Self Consistency Random Var 5 is developed by Yuhan123, a model with 13b parameters. 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 Vicuna 13B Self Consistency Random Var 5?

Vicuna 13B Self Consistency Random Var 5 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Vicuna 13B Self Consistency Random Var 5 by Yuhan123 — VRAM 26GB, 4K context

Name: Vicuna 13B Self Consistency Random Var 5
Author: Yuhan123

Vicuna 13B Self Consistency Random Var 5 is an open-source language model by Yuhan123. Features: 13b LLM, VRAM: 26GB, Context: 4K, LLM Explorer Score: 0.17.

Arxiv:1910.09700 Conversational Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Yuhan123/vicuna-13b-self_consistency_random_var_5

Vicuna 13B Self Consistency Random Var 5 Parameters and Internals

LLM Name	Vicuna 13B Self Consistency Random Var 5
Repository 🤗	https://huggingface.co/Yuhan123/vicuna-13b-self_consistency_random_var_5
Model Size	13b
Required VRAM	26 GB
Updated	2026-10-09
Maintainer	Yuhan123
Model Type	llama
Model Files	5.0 GB: 1-of-6 5.0 GB: 2-of-6 5.0 GB: 3-of-6 4.9 GB: 4-of-6 4.9 GB: 5-of-6 1.2 GB: 6-of-6
Model Architecture	LlamaForCausalLM
Context Length	4096
Model Max Length	4096
Transformers Version	4.44.0
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

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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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