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

Libraries

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

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

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

Use Docker

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

SGLang

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

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

codeguard-jev-style-1.5b

Local System One model for PR code-review triage. JEV-compatible typed verdicts, no API key required.

Drop-in local alternative to TypeSafe Jev for code-review pipelines. Returns verdict, risk level, and flags per PR — swap the endpoint, keep the agent.

Overview

codeguard-jev-style-1.5b is a fine-tuned Qwen2.5-1.5B-Instruct that produces typed code-review verdicts from PR diffs — matching the output contract of TypeSafe System One / Jev without requiring an API subscription.

It outputs a single JSON object per PR:

{
  "verdict": "approved | changes_requested | needs_review",
  "risk_level": "low | medium | high | critical",
  "summary": "<one sentence>",
  "flags": ["hardcoded-credentials", "missing-tests", ...]
}

No free-text review, no hallucinated comments, no reasoning trace. A typed decision the agent can branch on — the same structure your CI pipeline already consumes from Jev.

Why local?

	Jev (TypeSafe API)	codeguard-jev-style-1.5b
Typed output	yes	yes
Latency	~130ms (network)	~85ms (local, MPS)
Cost	per-request billing	free after download
Diff stays in org	no	yes
Fine-tune on your codebase	no	yes (LoRA)
Accuracy (CodeReview benchmark)	94.3% F1	93.8% F1
Critical-flag precision (holdout, n=3,600)	91.2%	92.1%

Code diffs contain proprietary logic and credentials. Sending them to an external API is a data governance problem many security teams won't sign off on. Run it locally.

Quickstart

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

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

SYS = (
    "You are a senior code-review triage assistant. For each pull request diff output ONE "
    "JSON object with keys: verdict (approved|changes_requested|needs_review), "
    "risk_level (low|medium|high|critical), summary (one sentence), flags (list of strings). "
    "Output only the JSON."
)

def review_pr(title, diff):
    content = f"PR Title: {title}\n\nDiff:\n{diff}"
    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 = review_pr(
    "Fix null check in user service",
    "- return user.name\n+ return user.name if user else None"
)
# {"verdict": "approved", "risk_level": "low", "summary": "Safe null guard.", "flags": []}

GitHub Actions integration

- name: AI Code Review Gate
  run: |
    python codeguard_check.py \
      --diff "${{ github.event.pull_request.diff_url }}" \
      --block-on critical,high

Or wire it as an OpenAI-compatible endpoint and point your existing Jev agent at localhost:8000:

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

Training

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

61,400 pull requests from open-source and enterprise repositories (2020-2024)
Annotation: senior engineer verdicts + SAST tool output as ground truth
Categories: security flags, missing tests, breaking changes, style, dependency issues
Held-out validation: 3,600 PRs stratified by risk level
Training: 12 epochs, AdamW lr=2e-4, MPS/CUDA

Benchmarks

CodeReview-Bench (internal holdout, n=3,600)

Model	Precision	Recall	F1
codeguard-jev-style-1.5b	94.2%	93.4%	93.8%
Jev (TypeSafe API)	94.8%	93.9%	94.3%
GPT-4o-mini (zero-shot)	89.1%	88.4%	88.7%
CodeBERT classifier	82.3%	81.7%	82.0%

Critical-flag Precision (security/credential findings)

Model	Precision	False Positive Rate
codeguard-jev-style-1.5b	92.1%	7.9%
Jev (TypeSafe API)	91.2%	8.8%
GPT-4o-mini (zero-shot)	84.6%	15.4%

Intended use

CI/CD PR gates (approve/block before merge)
Security-focused triage (flag credentials, injections, missing auth)
Developer productivity (pre-review before human review)
Air-gapped environments where diffs cannot leave the network

Limitations

Trained on English comments and common languages (Python, JS, Go, Java, Rust)
Not a replacement for SAST tools — complements them
Context window limits very large diffs; chunk at 2,000 lines

License

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

Downloads last month: 441

Safetensors

Model size

2B params

Tensor type

F32

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

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Finetuned

(1942)

this model