What are the hardware requirements for Zephyr Rag Agent?

Zephyr Rag Agent requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Zephyr Rag Agent and how large is it?

Zephyr Rag Agent is developed by erbacher, a model with 7b 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?

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

Zephyr Rag Agent by erbacher — VRAM 0.2GB

Name: Zephyr Rag Agent
Author: erbacher

Zephyr Rag Agent is an open-source language model by erbacher. Features: 7b LLM, VRAM: 0.2GB, License: mit, LLM Explorer Score: 0.12.

Adapter Alignment-handbook Base model:adapter:huggingface... Base model:huggingfaceh4/zephy... Dataset:erbacher/rag-and-roll Finetuned Generated from trainer Lora Mistral Peft Region:us Safetensors Sft Trl

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

Zephyr Rag Agent Parameters and Internals

Training Details

Data Sources:

erbacher/rag-and-roll

LLM Name	Zephyr Rag Agent
Repository 🤗	https://huggingface.co/erbacher/zephyr-rag-agent
Base Model(s)	Zephyr 7B Beta HuggingFaceH4/zephyr-7b-beta
Model Size	7b
Required VRAM	0.2 GB
Updated	2025-10-13
Maintainer	erbacher
Model Files	0.2 GB 0.0 GB
Model Architecture	Adapter
License	mit
Model Max Length	2048
Is Biased	none
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|k_proj\|gate_proj\|up_proj\|down_proj\|v_proj\|o_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	32

Rank the Zephyr Rag Agent 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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Note: green Score (e.g. "73.2") means that the model is better than erbacher/zephyr-rag-agent.