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 / config.json

manu-specific

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	{
	"architectures": [
	"BertForSequenceClassification"
	],
	"attention_probs_dropout_prob": 0.1,
	"classifier_dropout": null,
	"dtype": "float32",
	"gradient_checkpointing": false,
	"hidden_act": "gelu",
	"hidden_dropout_prob": 0.1,
	"hidden_size": 768,
	"id2label": {
	"0": "False",
	"1": "True"
	},
	"initializer_range": 0.02,
	"intermediate_size": 3072,
	"label2id": {
	"False": 0,
	"True": 1
	},
	"layer_norm_eps": 1e-12,
	"max_position_embeddings": 512,
	"model_type": "bert",
	"num_attention_heads": 12,
	"num_hidden_layers": 12,
	"pad_token_id": 0,
	"position_embedding_type": "absolute",
	"problem_type": "single_label_classification",
	"transformers_version": "4.57.3",
	"type_vocab_size": 2,
	"use_cache": true,
	"vocab_size": 30522
	}