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

specific-AI/email-agent-triage

A compact BERT email triage classifier distilled with Specific AI. It assigns each email to one of five action-oriented categories so agentic workflows can decide whether to reply, archive, or take no action.


Task	Single-label text classification
Base model	`bert-base-uncased`
Training data	~15,000 examples
License	MIT

Input format

Examples were trained on emails formatted as plain text with From, Subject, and body (blank line between the headers and the body):

From: <from>
Subject: <subject>

<body>

Pass inputs in this same shape at inference time for best results.

Labels

Label	Meaning	Suggested next action
URGENT	Requires immediate attention (e.g. critical system failure, hard deadline right now).	Reply
NEEDS_RESPONSE	A task or response is owed, but it is not a drop-everything emergency.	Reply
PROMOTIONAL	Bulk mail, unsolicited promotions, or newsletters.	Archive
PERSONAL	Non-business, personal communications.	None
FYI	Informational only — the recipient should know, but no reply is required.	None

Evaluation

Compared against gpt-5.4-mini as a teacher / baseline on the same evaluation set:

Metric	gpt-5.4-mini	SpecificAI
Accuracy	0.693	0.720
Precision	0.810	0.763
Recall	0.693	0.720
F1 score	0.693	0.716

Repository contents

This card ships both a full Hugging Face checkpoint and GGUF-ready artifacts:

Full BertForSequenceClassification weights (model.safetensors) + tokenizer
Head layers as NumPy files (pooler_*.npy, classifier_*.npy) for GGUF / Lemonade fusion
Encoder GGUF: bert-base-only.gguf (CLS pooling; use with raw / unnormalized embeddings)

Quick start — Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "specific-AI/email-agent-triage"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

text = """From: ops@example.com
Subject: Production outage

Production is down — please escalate immediately."""
inputs = tokenizer(text, return_tensors="pt", truncation=True)
with torch.no_grad():
    logits = model(**inputs).logits
pred = model.config.id2label[int(logits.argmax(-1))]
print(pred)

Quick start — Lemonade + specific-ai-tools

When running the GGUF encoder through Lemonade Server:

pip install specific-ai-tools

from specific_ai_tools.embedding_heads import LemonadeEmbeddingClassifier

classifier = LemonadeEmbeddingClassifier(
    lemonade_model_name="user.email-agent-triage",
    checkpoint="specific-AI/email-agent-triage:bert-base-only.gguf",
    lemonade_base_url="http://localhost:13305",
)

text = """From: user@example.com
Subject: Billing question

Please escalate this ticket to billing."""
result = classifier.predict_one(text)
print(result.predicted_labels, result.predicted_confidences)

See the Specific AI toolkit docs for llama-cpp and other embedding backends.

Intended use

Email / inbox agent triage in production or on-device / CPU deployments
Routing messages into reply / archive / no-action queues

Out of scope: legal advice, medical triage, or safety-critical decisions without human review. Labels reflect email workflow intent, not sender identity verification.

About Us

Specific AI is the automatic SLM distillation platform that turns task prompts into production-grade small language models in days — not weeks — so your subject matter experts can ship models without waiting on scarce data-science bandwidth.

We help enterprises move agentic AI from prototype to production with SLMs that are typically 1,000×–10,000× smaller than teacher LLMs, run in milliseconds on CPUs or edge devices, and deliver the same or better task quality at a fraction of the cost — self-hosted on your cloud or downloaded for your own inference stack.

Prompt → Distill → Deploy. Bring your prompt and data, drop them into Specific AI, and get a validated small model ready to test and ship.

Ready to create SLMs at scale? Visit specific.ai.

License

MIT — see LICENSE.

Downloads last month: 35

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for specific-AI/email-agent-triage

Base model

google-bert/bert-base-uncased

Quantized

(29)

this model