What are the hardware requirements for Llama Email Fraud Detector?

Llama Email Fraud Detector requires approximately 6.4 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama Email Fraud Detector and how large is it?

Llama Email Fraud Detector is developed by cunxin, a model with 3b 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 Llama Email Fraud Detector?

Llama Email Fraud Detector is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama Email Fraud Detector by cunxin — VRAM 6.4GB, 128K context

Name: Llama Email Fraud Detector
Author: cunxin

Llama Email Fraud Detector is an open-source language model by cunxin. Features: 3b LLM, VRAM: 6.4GB, Context: 128K, License: llama3.2, Instruction-Based, LLM Explorer Score: 0.22.

Base model:adapter:meta-llama/... Base model:meta-llama/llama-3.... Conversational Cybersecurity Dataset:setfit/enron spam Email En Finetuned Fraud-detection Instruct Llama Lora Model-index Phishing Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/cunxin/llama-email-fraud-detector

Llama Email Fraud Detector Parameters and Internals

LLM Name	Llama Email Fraud Detector
Repository 🤗	https://huggingface.co/cunxin/llama-email-fraud-detector
Base Model(s)	meta-llama/Llama-3.2-3B-Instruct meta-llama/Llama-3.2-3B-Instruct
Model Size	3b
Required VRAM	6.4 GB
Updated	2026-08-06
Maintainer	cunxin
Model Type	llama
Instruction-Based	Yes
Model Files	6.4 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	llama3.2
Context Length	131072
Model Max Length	131072
Transformers Version	5.4.0
Tokenizer Class	TokenizersBackend
Padding Token	<\|eot_id\|>
Vocabulary Size	128256
LoRA Model	Yes

Rank the Llama Email Fraud Detector capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
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Multi-Language Support and Translation

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Note: green Score (e.g. "73.2") means that the model is better than cunxin/llama-email-fraud-detector.