Instructions to use specific-AI/email-agent-phishing-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use specific-AI/email-agent-phishing-detection with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="specific-AI/email-agent-phishing-detection")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("specific-AI/email-agent-phishing-detection")
model = AutoModelForSequenceClassification.from_pretrained("specific-AI/email-agent-phishing-detection", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use specific-AI/email-agent-phishing-detection with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf specific-AI/email-agent-phishing-detection
# Run inference directly in the terminal:
llama cli -hf specific-AI/email-agent-phishing-detection

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf specific-AI/email-agent-phishing-detection
# Run inference directly in the terminal:
llama cli -hf specific-AI/email-agent-phishing-detection

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf specific-AI/email-agent-phishing-detection
# Run inference directly in the terminal:
./llama-cli -hf specific-AI/email-agent-phishing-detection

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf specific-AI/email-agent-phishing-detection
# Run inference directly in the terminal:
./build/bin/llama-cli -hf specific-AI/email-agent-phishing-detection

Use Docker

docker model run hf.co/specific-AI/email-agent-phishing-detection

LM Studio
Jan
Ollama
How to use specific-AI/email-agent-phishing-detection with Ollama:
```
ollama run hf.co/specific-AI/email-agent-phishing-detection
```
Unsloth Desktop
Docker Model Runner
How to use specific-AI/email-agent-phishing-detection with Docker Model Runner:
```
docker model run hf.co/specific-AI/email-agent-phishing-detection
```

Lemonade

How to use specific-AI/email-agent-phishing-detection with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull specific-AI/email-agent-phishing-detection

Run and chat with the model

lemonade run user.email-agent-phishing-detection-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

email-agent-phishing-detection / README.md

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metadata

license: mit
language:
  - en
library_name: transformers
pipeline_tag: text-classification
tags:
  - email
  - phishing
  - classification
  - bert
  - specific-ai
  - gguf
base_model: google-bert/bert-base-uncased

specific-AI/email-agent-phishing-detection

A compact BERT phishing detector distilled with Specific AI. It classifies email content as phishing or not, for use in email agents and security-aware inbox workflows.


Task	Single-label text classification
Base model	`bert-base-uncased`
Training data	~15,000 examples
License	MIT

Input format

Examples were trained on emails formatted as plain text with From, Subject, and body (blank line between the headers and the body):

From: <from>
Subject: <subject>

<body>

Pass inputs in this same shape at inference time for best results.

Labels

Label	Meaning
True	Phishing detected
False	Phishing was not detected

Evaluation

Compared against gpt-5.4-mini as a teacher / baseline on the same evaluation set:

Metric	gpt-5.4-mini	SpecificAI
Accuracy	0.971	0.975
Precision	0.976	0.975
Recall	0.971	0.975
F1 score	0.972	0.975

Repository contents

This card ships both a full Hugging Face checkpoint and GGUF-ready artifacts:

Full BertForSequenceClassification weights (model.safetensors) + tokenizer
Head layers as NumPy files (pooler_*.npy, classifier_*.npy) for GGUF / Lemonade fusion
Encoder GGUF: bert-base-only.gguf (CLS pooling; use with raw / unnormalized embeddings)

Quick start — Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "specific-AI/email-agent-phishing-detection"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

text = """From: security@paypa1-support.com
Subject: Your account will be locked

Verify your password at http://example-phish.test/login to keep access."""
inputs = tokenizer(text, return_tensors="pt", truncation=True)
with torch.no_grad():
    logits = model(**inputs).logits
pred = model.config.id2label[int(logits.argmax(-1))]
print(pred)  # "True" or "False"

Quick start — Lemonade + specific-ai-tools

When running the GGUF encoder through Lemonade Server:

pip install specific-ai-tools

from specific_ai_tools.embedding_heads import LemonadeEmbeddingClassifier

classifier = LemonadeEmbeddingClassifier(
    lemonade_model_name="user.email-agent-phishing-detection",
    checkpoint="specific-AI/email-agent-phishing-detection:bert-base-only.gguf",
    lemonade_base_url="http://localhost:13305",
)

text = """From: noreply@secure-mail-alert.com
Subject: Reset your password now

Click here to reset your password immediately."""
result = classifier.predict_one(text)
print(result.predicted_labels, result.predicted_confidences)

See the Specific AI toolkit docs for llama-cpp and other embedding backends.

Intended use

Email / inbox agents that need a fast on-device or CPU phishing signal
Pre-filter or assistive scoring alongside other security controls

Out of scope: sole authority for blocking, quarantine, or legal determinations. Treat outputs as a high-throughput classifier signal and keep human / policy review in the loop for high-impact actions.

About Us

Specific AI is the automatic SLM distillation platform that turns task prompts into production-grade small language models in days — not weeks — so your subject matter experts can ship models without waiting on scarce data-science bandwidth.

We help enterprises move agentic AI from prototype to production with SLMs that are typically 1,000×–10,000× smaller than teacher LLMs, run in milliseconds on CPUs or edge devices, and deliver the same or better task quality at a fraction of the cost — self-hosted on your cloud or downloaded for your own inference stack.

Prompt → Distill → Deploy. Bring your prompt and data, drop them into Specific AI, and get a validated small model ready to test and ship.

Ready to create SLMs at scale? Visit specific.ai.

License

MIT — see LICENSE.