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")

# pip install -U transformers accelerate
# 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 embeddings server:
llama serve -hf specific-AI/email-agent-phishing-detection --embeddings

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible embeddings server:
llama serve -hf specific-AI/email-agent-phishing-detection --embeddings

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible embeddings server:
./llama-server -hf specific-AI/email-agent-phishing-detection --embeddings

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 embeddings server:
./build/bin/llama-server -hf specific-AI/email-agent-phishing-detection --embeddings

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 / training_args.bin

manu-specific

Upload 16 files

aef4ff7 verified 3 months ago

History Blame Contribute Delete

5.91 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 4680b611c9bacda34e04c241bad22578deb93396df6ddfc199200ccbe227bbe8
Size of remote file:: 5.91 kB
SHA256:: 5b3e8784bad28e4d48ad30e61a905f913cb182018f0244adf5b0a7e2bd4332d9

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.