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

Libraries

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

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

pipe = pipeline("text-generation", model="JackKozmo29/depguard-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/depguard-jev-style-1.5b")
model = AutoModelForCausalLM.from_pretrained("JackKozmo29/depguard-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/depguard-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/depguard-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/depguard-jev-style-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

depguard-jev-style-1.5b

Local System One model for dependency security review. JEV-compatible typed verdicts, no API key required.

Drop-in local alternative to TypeSafe Jev for supply-chain review pipelines. Returns safe/caution/block per package update — swap the endpoint, keep the agent.

Overview

depguard-jev-style-1.5b is a fine-tuned Qwen2.5-1.5B-Instruct that produces typed dependency security verdicts — matching the output contract of TypeSafe System One / Jev for supply-chain review use cases.

It outputs a single JSON object per package update:

{
  "verdict": "safe | caution | block",
  "reason": "<one sentence>",
  "cve_count": 0,
  "recommendation": "upgrade | pin | replace | accept | reject"
}

Feed it Dependabot PRs, Renovate changelogs, or npm audit output. Get a typed decision the agent can act on — auto-merge safe updates, block risky ones, queue caution for human review.

Why local?

	Jev (TypeSafe API)	depguard-jev-style-1.5b
Typed output	yes	yes
Latency	~125ms (network)	~82ms (local, MPS)
Cost	per-request billing	free after download
Data leaves org	yes	no
Fine-tune on internal allowlist	no	yes (LoRA)
Accuracy (OSV-Bench)	96.1% F1	95.7% F1
Block precision (malicious pkg holdout)	94.3%	95.1%

Dependency metadata often contains internal package names and version strings that reveal your technology stack. Run the classifier locally.

Quickstart

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

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

SYS = (
    "You are a dependency security review assistant. For each package update output ONE "
    "JSON object with keys: verdict (safe|caution|block), reason (one sentence), "
    "cve_count (integer), recommendation (upgrade|pin|replace|accept|reject). "
    "Output only the JSON."
)

def review_dep(name, registry, old_ver, new_ver, description, license):
    content = (f"Package: {name}\nRegistry: {registry}\n"
               f"Current: {old_ver} -> Proposed: {new_ver}\n"
               f"Description: {description}\nLicense: {license}")
    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=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 = review_dep("lodash", "npm", "4.17.15", "4.17.21",
                    "Utility library", "MIT")
# {"verdict": "safe", "reason": "Security patch for CVE-2021-23337.", "cve_count": 1, "recommendation": "upgrade"}

Renovate / Dependabot integration

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

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

Auto-merge pattern:

verdict = review_dep(name, registry, old_ver, new_ver, desc, lic)
if verdict["verdict"] == "safe" and verdict["cve_count"] == 0:
    merge_pr(pr_id)
elif verdict["verdict"] == "block":
    close_pr(pr_id, reason=verdict["reason"])
else:
    assign_for_review(pr_id)

Training

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

92,000 package updates from PyPI, npm, Maven, and Go modules (2018-2024)
Ground truth: OSV database, GitHub Advisory Database, manual security team labels
Includes: known malicious packages (event-stream, ua-parser-js, colors 1.4.44-liberty, etc.)
Held-out validation: 4,800 packages stratified by verdict
Training: 12 epochs, AdamW lr=2e-4, MPS/CUDA

Benchmarks

OSV-Bench (CVE-annotated package updates, n=4,800)

Model	Precision	Recall	F1
depguard-jev-style-1.5b	96.0%	95.4%	95.7%
Jev (TypeSafe API)	96.4%	95.9%	96.1%
GPT-4o-mini (zero-shot)	90.2%	89.6%	89.9%
OSV-Scanner (rule-based)	98.1%	71.3%	82.7%

Malicious Package Detection (supply-chain attack samples)

Model	Block Rate	False Positive Rate
depguard-jev-style-1.5b	95.1%	4.9%
Jev (TypeSafe API)	94.3%	5.7%
OSV-Scanner (rule-based)	61.2%	1.1%

Note: rule-based scanners miss novel malicious packages not yet in the CVE database. The LLM classifier catches behavioral and metadata signals that signature-based tools miss.

Intended use

Dependabot / Renovate PR auto-approval pipelines
Supply-chain security gates in CI/CD
Package allowlist enforcement
Air-gapped environments (no external CVE API calls needed)

Limitations

Verdict is based on metadata and description — not static analysis of package contents
Novel malicious packages with clean metadata may score safe
Combine with OSV-Scanner for defense in depth

License

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

Downloads last month: 63,299

Safetensors

Model size

2B params

Tensor type

F32

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

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Finetuned

(1994)

this model