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Shanvit/
email-triage-slm

Name: email-triage-slm
Availability: InStock

jason23322/high-accuracy-email-classifier

Qwen/Qwen3-4B-Instruct-2507

finetune

mit

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Email Triage SLM

LoRA fine-tuned small language model for email triage. Classifies incoming emails and generates structured triage outputs including classification, priority, suggested actions, and draft responses.

Model Details

Base Model: Qwen/Qwen3-4B-Instruct-2507 (4B parameters)
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Task: Email classification and structured triage
License: MIT

Training Details

Hyperparameters

Parameter	Value
LoRA Rank	32
Learning Rate	1e-4
Batch Size	4
Epochs	3
Loss	Cross-entropy (prompt masked, completion weighted)

Training Data

Dataset: jason23322/high-accuracy-email-classifier
Categories: Spam, Promotions, Verify_code, Updates, Forum, Social_media
Format: Structured prompt/completion pairs with EOS tokens for stopping behavior

Output Format

The model produces structured triage output in four steps:

Step 1: Classification → {category}
Step 2: Priority → {High|Medium|Low}
Step 3: Suggested actions → {action_items}
Step 4: Drafted response → {draft_response}

Category-to-Priority Mapping:

verify_code → High priority, urgent action required
spam / promotions → Low priority, delete/mark as spam
updates → Medium priority, read and acknowledge
forum / social_media → Medium priority, read later

Usage

With Transformers + PEFT

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "Qwen/Qwen3-4B-Instruct-2507"
adapter_path = "path/to/adapters/email_triage_final"

model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_path)
tokenizer = AutoTokenizer.from_pretrained(base_model)

prompt = """You are an efficient admin assistant. Analyze this incoming email step-by-step and output in structured format:

Email:
Subject: Your order has shipped!
Hi [Customer Name], Your recent order #[ORDER NUMBER] has shipped and is on its way. You can track your package here: [Tracking Link]. Thanks for shopping with us!"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Inference API

When running the included FastAPI server:

bash

curl -X POST http://localhost:8000/inference \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "You are an efficient admin assistant. Analyze this incoming email step-by-step and output in structured format:\n\nEmail:\nSubject: Your order has shipped!\n...",
    "max_tokens": 256,
    "temperature": 0.7
  }'

Important: Use the exact prompt format above for best results. The model was trained with this structure.

Intended Use

Email inbox triage and prioritization
Administrative assistant workflows
Automated email classification and routing
Draft response generation for common email types

Limitations

Trained on a specific dataset (400 examples default); may not generalize to all email domains
Structured output format is fixed; customization requires retraining
Base model (Qwen3-4B) is hardcoded; adapter is not compatible with other base models
May misclassify edge cases or emails in languages underrepresented in training data

Bias & Safety

Inherits base model biases; evaluate outputs for your use case
Training data reflects the distribution of the source dataset
Not intended for high-stakes decisions without human review

Training Infrastructure

Trained using Tinker Labs distributed training API. For full training pipeline, dataset preparation, and local inference setup, see the project repository.

Citation

bibtex

@misc{email-triage-slm,
  title={Email Triage SLM: LoRA Fine-tuned Model for Email Classification},
  author={Shanvit S Shetty},
  year={2026},
  url={https://huggingface.co/your-username/email-triage-slm}
  howpublished={\url{https://github.com/Shanvit7/email-triage-slm}}
}

Repository: github.com/Shanvit7/email-triage-slm

Shanvit/email-triage-slm