Text Classification
Transformers
Safetensors
GGUF
English
bert
email
phishing
classification
specific-ai
text-embeddings-inference
feature-extraction
Instructions to use specific-AI/email-agent-phishing-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - llama-cpp-python
How to use specific-AI/email-agent-phishing-detection with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="specific-AI/email-agent-phishing-detection", filename="bert-base-only.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- 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 Studio
How to use specific-AI/email-agent-phishing-detection with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for specific-AI/email-agent-phishing-detection to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for specific-AI/email-agent-phishing-detection to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for specific-AI/email-agent-phishing-detection to start chatting
- Atomic Chat new
- 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
- Xet hash:
- 137494bb97ca7f65adf7bfd2526ac32bb83e2b44cc3c0032d22c338d6c1d30ac
- Size of remote file:
- 3.2 kB
- SHA256:
- 4f0642cc5fbc74d7f49f4d0a6e580885bb930082cef47883b2c3beb0d15d3bd2
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