What are the hardware requirements for Zephyr Rag Agent Webgpt?

Zephyr Rag Agent Webgpt requires approximately 40.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Zephyr Rag Agent Webgpt and how large is it?

Zephyr Rag Agent Webgpt is developed by erbacher, a model with 2.8b 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 Zephyr Rag Agent Webgpt?

Zephyr Rag Agent Webgpt is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Zephyr Rag Agent Webgpt by erbacher — VRAM 40.1GB

Name: Zephyr Rag Agent Webgpt
Author: erbacher

Zephyr Rag Agent Webgpt is an open-source language model by erbacher. Features: 2.8b LLM, VRAM: 40.1GB, LLM Explorer Score: 0.12.

Arxiv:1910.09700 Endpoints compatible Lora Region:us Safetensors Sharded Stablelm Tensorflow

Model Card on HF 🤗: https://huggingface.co/erbacher/zephyr-rag-agent-webgpt

Zephyr Rag Agent Webgpt Parameters and Internals

LLM Name	Zephyr Rag Agent Webgpt
Repository 🤗	https://huggingface.co/erbacher/zephyr-rag-agent-webgpt
Model Size	2.8b
Required VRAM	40.1 GB
Updated	2026-07-13
Maintainer	erbacher
Model Files	0.2 GB 5.0 GB: 1-of-3 5.0 GB: 1-of-6 5.0 GB: 2-of-3 4.9 GB: 2-of-6 1.2 GB: 3-of-3 5.0 GB: 3-of-6 5.0 GB: 4-of-6 4.8 GB: 5-of-6 4.2 GB: 6-of-6
Model Architecture	AutoModelForCausalLM
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	o_proj\|up_proj\|down_proj\|gate_proj\|v_proj\|k_proj\|q_proj
LoRA Alpha	16
LoRA Dropout	0.05
R Param	16

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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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