Instructions to use specific-AI/email-agent-phishing-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- 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
| { | |
| "best_global_step": 1176, | |
| "best_metric": 0.9635223336896785, | |
| "best_model_checkpoint": "/app/checkpoints/checkpoint-1176", | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 1176, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0, | |
| "eval_accuracy": 0.3837320574162679, | |
| "eval_class_metrics": { | |
| "0": { | |
| "accuracy": 0.3837320574162679, | |
| "confusion_matrix": { | |
| "fn": 1927, | |
| "fp": 5, | |
| "tn": 1200, | |
| "tp": 3 | |
| }, | |
| "f_beta_score": 0.0030959752321981426, | |
| "precision": 0.375, | |
| "recall": 0.0015544041450777201 | |
| }, | |
| "1": { | |
| "accuracy": 0.3837320574162679, | |
| "confusion_matrix": { | |
| "fn": 5, | |
| "fp": 1927, | |
| "tn": 3, | |
| "tp": 1200 | |
| }, | |
| "f_beta_score": 0.554016620498615, | |
| "precision": 0.38375439718580107, | |
| "recall": 0.995850622406639 | |
| } | |
| }, | |
| "eval_confusion_matrix": { | |
| "fn": 1932, | |
| "fp": 1932, | |
| "tn": 1203, | |
| "tp": 1203 | |
| }, | |
| "eval_f_beta_score": 0.21485335243986398, | |
| "eval_label_confusion_matrix": { | |
| "labels": [ | |
| "False", | |
| "True" | |
| ], | |
| "matrix": [ | |
| [ | |
| 3, | |
| 1927 | |
| ], | |
| [ | |
| 5, | |
| 1200 | |
| ] | |
| ] | |
| }, | |
| "eval_loss": 0.689696729183197, | |
| "eval_precision": 0.3783649277859299, | |
| "eval_recall": 0.3837320574162679, | |
| "eval_runtime": 19.5865, | |
| "eval_sample_size": 3135, | |
| "eval_samples_per_second": 160.059, | |
| "eval_steps_per_second": 20.014, | |
| "step": 0 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "grad_norm": 2.180086612701416, | |
| "hard_loss": 0.1800905573181808, | |
| "learning_rate": 2.288000034644329e-05, | |
| "loss": 0.1801, | |
| "step": 392 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.9623604465709729, | |
| "eval_class_metrics": { | |
| "0": { | |
| "accuracy": 0.9623604465709729, | |
| "confusion_matrix": { | |
| "fn": 72, | |
| "fp": 46, | |
| "tn": 1159, | |
| "tp": 1858 | |
| }, | |
| "f_beta_score": 0.9692227438706312, | |
| "precision": 0.9758403361344538, | |
| "recall": 0.9626943005181348 | |
| }, | |
| "1": { | |
| "accuracy": 0.9623604465709729, | |
| "confusion_matrix": { | |
| "fn": 46, | |
| "fp": 72, | |
| "tn": 1858, | |
| "tp": 1159 | |
| }, | |
| "f_beta_score": 0.951559934318555, | |
| "precision": 0.9415109666937449, | |
| "recall": 0.9618257261410789 | |
| } | |
| }, | |
| "eval_confusion_matrix": { | |
| "fn": 118, | |
| "fp": 118, | |
| "tn": 3017, | |
| "tp": 3017 | |
| }, | |
| "eval_f_beta_score": 0.9624336894813962, | |
| "eval_label_confusion_matrix": { | |
| "labels": [ | |
| "False", | |
| "True" | |
| ], | |
| "matrix": [ | |
| [ | |
| 1858, | |
| 72 | |
| ], | |
| [ | |
| 46, | |
| 1159 | |
| ] | |
| ] | |
| }, | |
| "eval_loss": 0.13664375245571136, | |
| "eval_precision": 0.9626451558550106, | |
| "eval_recall": 0.9623604465709729, | |
| "eval_runtime": 19.7068, | |
| "eval_sample_size": 3135, | |
| "eval_samples_per_second": 159.082, | |
| "eval_steps_per_second": 19.892, | |
| "step": 392 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "grad_norm": 4.48259162902832, | |
| "hard_loss": 0.10730842969734317, | |
| "learning_rate": 2.1675951462592336e-05, | |
| "loss": 0.1073, | |
| "step": 784 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.9588516746411483, | |
| "eval_class_metrics": { | |
| "0": { | |
| "accuracy": 0.9588516746411483, | |
| "confusion_matrix": { | |
| "fn": 51, | |
| "fp": 78, | |
| "tn": 1127, | |
| "tp": 1879 | |
| }, | |
| "f_beta_score": 0.9668124517622846, | |
| "precision": 0.9601430761369443, | |
| "recall": 0.9735751295336788 | |
| }, | |
| "1": { | |
| "accuracy": 0.9588516746411483, | |
| "confusion_matrix": { | |
| "fn": 78, | |
| "fp": 51, | |
| "tn": 1879, | |
| "tp": 1127 | |
| }, | |
| "f_beta_score": 0.9458665547629039, | |
| "precision": 0.9567062818336163, | |
| "recall": 0.9352697095435685 | |
| } | |
| }, | |
| "eval_confusion_matrix": { | |
| "fn": 129, | |
| "fp": 129, | |
| "tn": 3006, | |
| "tp": 3006 | |
| }, | |
| "eval_f_beta_score": 0.9587614769985673, | |
| "eval_label_confusion_matrix": { | |
| "labels": [ | |
| "False", | |
| "True" | |
| ], | |
| "matrix": [ | |
| [ | |
| 1879, | |
| 51 | |
| ], | |
| [ | |
| 78, | |
| 1127 | |
| ] | |
| ] | |
| }, | |
| "eval_loss": 0.1417260617017746, | |
| "eval_precision": 0.9588220754557608, | |
| "eval_recall": 0.9588516746411483, | |
| "eval_runtime": 19.5272, | |
| "eval_sample_size": 3135, | |
| "eval_samples_per_second": 160.545, | |
| "eval_steps_per_second": 20.075, | |
| "step": 784 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "grad_norm": 2.5649197101593018, | |
| "hard_loss": 0.0743093061087444, | |
| "learning_rate": 2.047190257874138e-05, | |
| "loss": 0.0743, | |
| "step": 1176 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9636363636363636, | |
| "eval_class_metrics": { | |
| "0": { | |
| "accuracy": 0.9636363636363636, | |
| "confusion_matrix": { | |
| "fn": 38, | |
| "fp": 76, | |
| "tn": 1129, | |
| "tp": 1892 | |
| }, | |
| "f_beta_score": 0.9707542329399692, | |
| "precision": 0.9613821138211383, | |
| "recall": 0.9803108808290155 | |
| }, | |
| "1": { | |
| "accuracy": 0.9636363636363636, | |
| "confusion_matrix": { | |
| "fn": 76, | |
| "fp": 38, | |
| "tn": 1892, | |
| "tp": 1129 | |
| }, | |
| "f_beta_score": 0.9519392917369308, | |
| "precision": 0.9674378748928878, | |
| "recall": 0.9369294605809129 | |
| } | |
| }, | |
| "eval_confusion_matrix": { | |
| "fn": 114, | |
| "fp": 114, | |
| "tn": 3021, | |
| "tp": 3021 | |
| }, | |
| "eval_f_beta_score": 0.9635223336896785, | |
| "eval_label_confusion_matrix": { | |
| "labels": [ | |
| "False", | |
| "True" | |
| ], | |
| "matrix": [ | |
| [ | |
| 1892, | |
| 38 | |
| ], | |
| [ | |
| 76, | |
| 1129 | |
| ] | |
| ] | |
| }, | |
| "eval_loss": 0.1422441303730011, | |
| "eval_precision": 0.9637097668008697, | |
| "eval_recall": 0.9636363636363636, | |
| "eval_runtime": 19.6152, | |
| "eval_sample_size": 3135, | |
| "eval_samples_per_second": 159.825, | |
| "eval_steps_per_second": 19.985, | |
| "step": 1176 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 7840, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 20, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "EarlyStoppingCallback": { | |
| "args": { | |
| "early_stopping_patience": 3, | |
| "early_stopping_threshold": 0.01 | |
| }, | |
| "attributes": { | |
| "early_stopping_patience_counter": 2 | |
| } | |
| }, | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 9894422792340480.0, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |