Instructions to use specific-AI/email-agent-triage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use specific-AI/email-agent-triage with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="specific-AI/email-agent-triage")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("specific-AI/email-agent-triage")
model = AutoModelForSequenceClassification.from_pretrained("specific-AI/email-agent-triage", device_map="auto")

llama-cpp-python

How to use specific-AI/email-agent-triage with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="specific-AI/email-agent-triage",
	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-triage 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-triage
# Run inference directly in the terminal:
llama cli -hf specific-AI/email-agent-triage

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-triage
# Run inference directly in the terminal:
llama cli -hf specific-AI/email-agent-triage

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-triage
# Run inference directly in the terminal:
./llama-cli -hf specific-AI/email-agent-triage

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-triage
# Run inference directly in the terminal:
./build/bin/llama-cli -hf specific-AI/email-agent-triage

Use Docker

docker model run hf.co/specific-AI/email-agent-triage

LM Studio
Jan
Ollama
How to use specific-AI/email-agent-triage with Ollama:
```
ollama run hf.co/specific-AI/email-agent-triage
```

Unsloth Studio

How to use specific-AI/email-agent-triage 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-triage 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-triage 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-triage to start chatting

Atomic Chat new
Docker Model Runner
How to use specific-AI/email-agent-triage with Docker Model Runner:
```
docker model run hf.co/specific-AI/email-agent-triage
```

Lemonade

How to use specific-AI/email-agent-triage with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull specific-AI/email-agent-triage

Run and chat with the model

lemonade run user.email-agent-triage-{{QUANT_TAG}}

List all available models

lemonade list

email-agent-triage / metadata.json

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{"task_type": "ClassificationResponse", "label_names": ["FYI", "NEEDS_RESPONSE", "PERSONAL", "PROMOTIONAL", "URGENT"], "client_id": "40b3a00e282ee2db1338c3bba881b99125fc3ba1d7f5c29232ce1ad63fab1e75", "llm_usecase_id": "6a59bb2dde331510eb8ff454", "distillation_event_id": "6a5e4173194ffc4db094fefa", "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"]}