🧠
Mezzo Prompt Guard V2 Base Gguf model by mradermacher
⭐ 37.3
🧠
Model

Mezzo Prompt Guard V2 Base Gguf

by mradermacher hf-model--mradermacher--mezzo-prompt-guard-v2-base-gguf
Nexus Index
37.3 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 20
R: Recency 95
Q: Quality 30
Tech Context
2 Params
4.096K Ctx
Vital Performance
484 DL / 30D
0.0%
Source →
Audited 37.3 FNI Score
Tiny 2B Params
4k Context
484 Downloads
8G GPU ~3GB Est. VRAM
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID hf-model--mradermacher--mezzo-prompt-guard-v2-base-gguf
License MIT
Provider huggingface
💾

Compute Threshold

~2.8GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__mradermacher__mezzo_prompt_guard_v2_base_gguf,
  author = {mradermacher},
  title = {Mezzo Prompt Guard V2 Base Gguf Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/mradermacher/mezzo-prompt-guard-v2-base-gguf}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
mradermacher. (2026). Mezzo Prompt Guard V2 Base Gguf [Model]. Free2AITools. https://huggingface.co/mradermacher/mezzo-prompt-guard-v2-base-gguf

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run mezzo-prompt-guard-v2-base-gguf
🤗 HF Download
huggingface-cli download mradermacher/mezzo-prompt-guard-v2-base-gguf
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
37.3
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 20
Recency (R) 95
Quality (Q) 30

đŸ’Ŧ Index Insight

FNI V2.0 for Mezzo Prompt Guard V2 Base Gguf: Semantic (S:50), Authority (A:0), Popularity (P:20), Recency (R:95), Quality (Q:30).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

Technical Deep Dive

About

static quants of https://huggingface.co/RyanStudio/Mezzo-Prompt-Guard-v2-Base

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link Type Size/GB Notes
GGUF Q2_K 0.3
GGUF Q3_K_S 0.3
GGUF Q3_K_M 0.3 lower quality
GGUF IQ4_XS 0.3
GGUF Q3_K_L 0.3
GGUF Q4_K_S 0.3 fast, recommended
GGUF Q4_K_M 0.3 fast, recommended
GGUF Q5_K_S 0.3
GGUF Q5_K_M 0.3
GGUF Q6_K 0.3 very good quality
GGUF Q8_0 0.4 fast, best quality
GGUF f16 0.7 16 bpw, overkill

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
484Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--mradermacher--mezzo-prompt-guard-v2-base-gguf
slug
mradermacher--mezzo-prompt-guard-v2-base-gguf
source
huggingface
author
mradermacher
license
MIT
tags
transformers, gguf, prompt, safety, prompt injections, prompt guard, guard, classification, en, tr, zh, hi, de, fr, base_model:ryanstudio/mezzo-prompt-guard-v2-base, license:mit, endpoints_compatible, region:us, feature-extraction

⚙ī¸ Technical Specs

architecture
null
params billions
2
context length
4,096
pipeline tag
vram gb
2.8
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
484
stars
0
forks
0

Data indexed from public sources. Updated daily.