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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JackKozmo29/sevclassifier-jev-style-1.5b")
model = AutoModelForCausalLM.from_pretrained("JackKozmo29/sevclassifier-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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JackKozmo29/sevclassifier-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/sevclassifier-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/sevclassifier-jev-style-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use JackKozmo29/sevclassifier-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/sevclassifier-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/sevclassifier-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/sevclassifier-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/sevclassifier-jev-style-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

sevclassifier-jev-style-1.5b

Local System One model for incident severity classification. JEV-compatible typed decisions, no API key required.

Drop-in local alternative to TypeSafe Jev for incident management pipelines. Returns SEV1-4, escalation flag, and blast radius — swap the endpoint, keep the agent.

Overview

sevclassifier-jev-style-1.5b is a fine-tuned Qwen2.5-1.5B-Instruct that produces typed incident severity classifications — matching the output contract of TypeSafe System One / Jev for incident triage pipelines.

It outputs a single JSON object per incident:

{
  "severity": "SEV1 | SEV2 | SEV3 | SEV4",
  "rationale": "<one sentence>",
  "escalate": true,
  "page_oncall": false,
  "affected_blast_radius": "<scope description>"
}

Feed it SIEM alerts, PagerDuty payloads, or Datadog monitor events. Get a typed decision the agent acts on immediately — page oncall for SEV1/2, create ticket for SEV3, suppress SEV4 noise.

Why local?

	Jev (TypeSafe API)	sevclassifier-jev-style-1.5b
Typed output	yes	yes
Latency	~115ms (network)	~76ms (local, MPS)
Cost	per-request billing	free after download
Alert data leaves org	yes	no
Fine-tune on your runbook	no	yes (LoRA)
Accuracy (SevBench)	93.6% F1	92.9% F1
SEV1 recall (no missed majors)	99.1%	98.8%

Incident alerts contain hostnames, IPs, service names, and error messages — the exact fingerprint of your infrastructure. Run the classifier on-prem.

Quickstart

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

tok = AutoTokenizer.from_pretrained("enterprise-ai-lab/sevclassifier-jev-style-1.5b")
model = AutoModelForCausalLM.from_pretrained(
    "enterprise-ai-lab/sevclassifier-jev-style-1.5b", dtype=torch.float32
).eval()

SYS = (
    "You are an incident severity classification assistant. For each incident report output ONE "
    "JSON object with keys: severity (SEV1|SEV2|SEV3|SEV4), rationale (one sentence), "
    "escalate (boolean), page_oncall (boolean), affected_blast_radius (string). "
    "SEV1=critical outage/breach, SEV2=major impact, SEV3=minor/degraded, SEV4=informational/noise. "
    "Output only the JSON."
)

def classify_incident(title, details):
    content = f"Incident: {title}\n\nDetails: {details}"
    msgs = [{"role": "system", "content": SYS}, {"role": "user", "content": content}]
    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=150, 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 = classify_incident(
    "Production database unreachable",
    "Primary RDS instance not responding. All pods returning 500. 12k users affected."
)
# {"severity": "SEV1", "rationale": "Full production outage.", "escalate": true, "page_oncall": true, "affected_blast_radius": "All production traffic"}

PagerDuty / OpsGenie integration

Wire it as an OpenAI-compatible endpoint and drop it into your existing alert routing agent:

# Before (Jev):
llm = ChatOpenAI(base_url="https://api.typesafe.ai/v1", api_key=JEV_KEY, model="jev-latest")
# After (SevClassifier local):
llm = ChatOpenAI(base_url="http://localhost:8000/v1", api_key="not-needed", model="sevclassifier-jev-style-1.5b")

Routing pattern:

verdict = classify_incident(title, details)
if verdict["severity"] in ("SEV1", "SEV2") and verdict["page_oncall"]:
    pagerduty.trigger(title, urgency="high")
elif verdict["severity"] == "SEV3":
    jira.create_ticket(title, priority="medium")
# SEV4: suppress, log only

Training

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

78,500 incidents from SRE runbooks, post-mortems, and SIEM alert exports (2019-2024)
Ground truth: retrospective human severity labels from SRE teams
Covers: infrastructure outages, security incidents, data pipeline failures, user-impacting bugs
Held-out validation: 4,200 incidents stratified by severity
Training: 12 epochs, AdamW lr=2e-4, MPS/CUDA

Benchmarks

SevBench (holdout, n=4,200)

Model	Precision	Recall	F1
sevclassifier-jev-style-1.5b	93.3%	92.5%	92.9%
Jev (TypeSafe API)	94.0%	93.3%	93.6%
GPT-4o-mini (zero-shot)	87.4%	86.8%	87.1%
Rule-based (keyword)	74.2%	68.9%	71.5%

SEV1 Recall (no missed critical incidents)

Model	SEV1 Recall	False Escalation Rate
sevclassifier-jev-style-1.5b	98.8%	6.2%
Jev (TypeSafe API)	99.1%	5.9%
GPT-4o-mini (zero-shot)	96.3%	11.4%

High SEV1 recall is the critical metric: a missed SEV1 is an undetected outage. False escalations are expensive but recoverable.

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

Model	p50	p95	p99
sevclassifier-jev-style-1.5b (local MPS)	76ms	108ms	129ms
Jev (TypeSafe API, US-West)	115ms	198ms	372ms

Intended use

Alert fatigue reduction (auto-suppress SEV4 noise)
Automatic PagerDuty escalation for SEV1/2
Incident ticket creation and prioritization
SOC triage assist for security incidents

Limitations

Trained on English incident descriptions
Severity scales vary by org — fine-tune on your own runbook for best results
Not a replacement for domain-specific monitoring thresholds

License

Apache 2.0. Base model (Qwen2.5-1.5B-Instruct) is subject to its own Qwen license.

Downloads last month: 20,426

Safetensors

Model size

2B params

Tensor type

F32

Model tree for JackKozmo29/sevclassifier-jev-style-1.5b

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct