Instructions to use JackKozmo29/mailguard-jev-style-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JackKozmo29/mailguard-jev-style-1.5b with Transformers:

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

pipe = pipeline("text-generation", model="JackKozmo29/mailguard-jev-style-1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JackKozmo29/mailguard-jev-style-1.5b")
model = AutoModelForCausalLM.from_pretrained("JackKozmo29/mailguard-jev-style-1.5b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JackKozmo29/mailguard-jev-style-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JackKozmo29/mailguard-jev-style-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JackKozmo29/mailguard-jev-style-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JackKozmo29/mailguard-jev-style-1.5b

SGLang

How to use JackKozmo29/mailguard-jev-style-1.5b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "JackKozmo29/mailguard-jev-style-1.5b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JackKozmo29/mailguard-jev-style-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "JackKozmo29/mailguard-jev-style-1.5b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JackKozmo29/mailguard-jev-style-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JackKozmo29/mailguard-jev-style-1.5b with Docker Model Runner:
```
docker model run hf.co/JackKozmo29/mailguard-jev-style-1.5b
```

mailguard-jev-style-1.5b

Local System One model for enterprise inbox triage. JEV-compatible typed decisions, no API key required.

Drop-in local alternative to TypeSafe Jev for inbox triage pipelines. Returns the same typed judgment schema — swap the endpoint, keep the agent.

Overview

mailguard-jev-style-1.5b is a fine-tuned Qwen2.5-1.5B-Instruct optimized to produce typed triage judgments over enterprise email — matching the output contract of TypeSafe System One / Jev without requiring an API subscription.

It outputs a single JSON object per email:

{
  "category": "finance | legal | hr | security | scheduling | general | newsletter",
  "summary": "<one sentence>",
  "forward_to": "<email address or null>"
}

No free-text generation, no reasoning trace, no hallucinated tool calls. Just a typed decision — the same structure your agent already consumes from Jev. Point your shim at localhost:8000 and nothing else changes.

Why local?

	Jev (TypeSafe API)	mailguard-jev-style-1.5b
Typed output	yes	yes
Latency	~120ms (network)	~80ms (local, MPS)
Cost	per-request billing	free after download
Data leaves org	yes	no
Fine-tune on your corpus	no	yes (LoRA)
Accuracy (TREC-07 spam)	97.1% F1	96.4% F1
Accuracy (Enron multi-label)	94.8% F1	95.2% F1
Routing precision (enterprise holdout, n=4,200)	93.6%	94.1%

The accuracy gap is within noise. The privacy and cost gap is not.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, json, re

tok = AutoTokenizer.from_pretrained("JackKozmo29/mailguard-jev-style-1.5b")
model = AutoModelForCausalLM.from_pretrained(
    "JackKozmo29/mailguard-jev-style-1.5b",
    dtype=torch.float32
).eval()

SYS = (
    "You are the inbox-triage assistant for an enterprise organization. "
    "For each email output ONE JSON object with keys: "
    "category (finance|legal|hr|security|scheduling|general|newsletter), "
    "summary (one sentence), forward_to (an email address or null). "
    "Output only the JSON."
)

def triage(subject, body):
    msgs = [{"role": "system", "content": SYS},
            {"role": "user",   "content": f"Subject: {subject}\n\n{body}"}]
    prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
    ids = tok(prompt, return_tensors="pt")
    with torch.no_grad():
        out = model.generate(**ids, max_new_tokens=120, do_sample=False,
                             pad_token_id=tok.eos_token_id)
    text = tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True)
    m = re.search(r"\{.*\}", text, re.DOTALL)
    return json.loads(m.group(0)) if m else {}

result = triage(
    "Wire approval needed",
    "Please approve the $84,500 wire to IBAN GB29... for the vendor settlement."
)
# {"category": "finance", "summary": "Wire transfer approval requested.", "forward_to": "finance@yourorg.com"}

OpenAI-shim + LangChain (drop-in for Jev pipelines)

If you already run a LangChain or langgraph agent against Jev, swap the base URL:

# Before (Jev):
llm = ChatOpenAI(base_url="https://api.typesafe.ai/v1", api_key=JEV_API_KEY, model="jev-latest")

# After (MailGuard local):
llm = ChatOpenAI(base_url="http://localhost:8000/v1", api_key="not-needed", model="mailguard-jev-style-1.5b")

Start the shim:

pip install fastapi uvicorn transformers torch
python serve_openai_shim.py

The shim script is included in this repository as serve_openai_shim.py.

Training

Fine-tuned with LoRA (r=16, alpha=32) on a curated enterprise email corpus:

84,200 emails across finance, legal, HR, security, scheduling, and general categories
Positive/negative balance: 38% sensitive routing triggers, 62% benign
Sources: anonymized Fortune 500 helpdesk exports (2019-2024), Enron corpus subset, synthetic augmentation
Held-out validation set: 4,200 emails, stratified by category
Training: 12 epochs, AdamW lr=2e-4, batch 4, MPS/CUDA

LoRA weights merged to base via merge_and_unload() and exported as model.safetensors. No pickle, no custom code — standard HF format, scannable with modelscan and picklescan.

Benchmarks

TREC 2007 Public Spam Corpus

Model	Precision	Recall	F1
mailguard-jev-style-1.5b	96.8%	96.1%	96.4%
Jev (TypeSafe API)	97.4%	96.9%	97.1%
MiniLM-L12 (baseline)	91.3%	90.7%	91.0%
GPT-4o-mini (zero-shot)	94.2%	93.8%	94.0%

Enron Multi-Label Classification

Model	Macro F1	Routing Accuracy
mailguard-jev-style-1.5b	95.2%	94.1%
Jev (TypeSafe API)	94.8%	93.6%
MiniLM-L12 (baseline)	88.1%	85.3%

Latency (Apple M2, 16GB, batch=1)

Model	p50	p95	p99
mailguard-jev-style-1.5b (local MPS)	78ms	112ms	134ms
Jev (TypeSafe API, US-West)	118ms	201ms	380ms

Intended use

Enterprise inbox automation (routing, archiving, compliance triage)
Drop-in local replacement for Jev in latency-sensitive or air-gapped deployments
Fine-tuning base for domain-specific routing rules (LoRA adapter support)