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 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

email-agent-phishing-detection / metadata.json

manu-specific

Upload 15 files

aedddf5 verified 15 days ago

Raw

History Blame Contribute Delete

479 Bytes

{"task_type": "ClassificationResponse", "label_names": ["False", "True"], "client_id": "40b3a00e282ee2db1338c3bba881b99125fc3ba1d7f5c29232ce1ad63fab1e75", "llm_usecase_id": "6a30dc55c8d00258ee3af462", "distillation_event_id": "6a4d4a3b194ffc4db094feb4", "tokenizer_files": ["tokenizer_config.json", "special_tokens_map.json", "vocab.txt", "added_tokens.json", "tokenizer.json"], "model_name": "bert-base-uncased", "input_names": ["input_ids", "attention_mask", "token_type_ids"]}