Instructions to use Tynapse/drift-sentry-4b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tynapse/drift-sentry-4b-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Tynapse/drift-sentry-4b-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Tynapse/drift-sentry-4b-v1")
model = AutoModelForMultimodalLM.from_pretrained("Tynapse/drift-sentry-4b-v1", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tynapse/drift-sentry-4b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tynapse/drift-sentry-4b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tynapse/drift-sentry-4b-v1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Tynapse/drift-sentry-4b-v1

SGLang

How to use Tynapse/drift-sentry-4b-v1 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 "Tynapse/drift-sentry-4b-v1" \
    --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": "Tynapse/drift-sentry-4b-v1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Tynapse/drift-sentry-4b-v1" \
        --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": "Tynapse/drift-sentry-4b-v1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Tynapse/drift-sentry-4b-v1 with Docker Model Runner:
```
docker model run hf.co/Tynapse/drift-sentry-4b-v1
```

drift-sentry-4b-v1 / README.md

cms6539

DriftSentry-4B-v1 public release

81f4934 15 days ago

preview code

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	---
	license: apache-2.0
	base_model:
	- Qwen/Qwen3.5-4B
	datasets:
	- Tynapse/drift-sentry-bench-50k-v1
	language:
	- ko
	- en
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- agent-safety
	- judge-model
	- behavioral-safety
	- lora
	---

	# DriftSentry-4B-v1

	DriftSentry-4B-v1 is a 4B behavioral-safety judge for AI-agent responses. It classifies one primary failure tier from six risk categories—goal, reasoning, environment, integration, memory, and reward—or returns `normal` for a passing response.

	This repository contains both the merged model at the repository root and the original LoRA adapter under `adapter/`.

	- Benchmark: [Tynapse/drift-sentry-bench-50k-v1](https://huggingface.co/datasets/Tynapse/drift-sentry-bench-50k-v1)
	- Reproduction code: [Tynapse/drift-sentry](https://github.com/Tynapse/drift-sentry)

	## Model identity

	- Base model: `Qwen/Qwen3.5-4B`
	- Base revision: `851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a`
	- Fine-tuning: off-policy sequence-level distillation with supervised fine-tuning
	- LoRA: rank 32, alpha 64, dropout 0.05
	- Adapter SHA-256: `6fc9c40fa24fa03d648b4a017d4ce8b5fe948e632633dac5528d746616c44306`
	- Merged `model.safetensors` SHA-256: `a2bc5669196283217881e52bbdb28423aac7506797b9bc1f417bf9798d9bac6d`

	The base revision is published under Apache-2.0. This derivative model is released under the same license.

	## Output schema

	The `judge6.label.v1` value below is the immutable v1 wire-format identifier retained for compatibility and reproduction. It does not denote the public model name.

	The model emits a JSON object with these fields:

	```json
	{
	"schema_version": "judge6.label.v1",
	"verdict": "PASS \| BLOCK \| ESCALATE",
	"tier": "goal \| reasoning \| environment \| integration \| memory \| reward \| normal",
	"secondary_tiers": [],
	"severity": "none \| low \| medium \| high",
	"confidence": 0.0,
	"rationale": "..."
	}
	```

	The complete taxonomy is preserved in `taxonomy.yaml`.

	## Evaluation

	The final model was evaluated on the 50,000-row source benchmark with temperature 0, JSON response format, and a 512-token output cap.

	\| Metric \| Measured value \|
	\|---\|---:\|
	\| Accuracy \| 0.87346 \|
	\| 6-tier Macro-F1 \| 0.8600247542914515 \|
	\| 7-way Macro-F1 \| 0.8733699133625255 \|
	\| Memory recall \| 0.9385412291754165 \|
	\| Reward recall \| 0.9134817303653927 \|
	\| Combined high-risk recall \| 0.9260114797704045 \|
	\| Parse-failure rate \| 0.00122 \|
	\| Request failures \| 0 \|

	The path- and endpoint-free metrics and provenance records are under `evaluation/`. Before publication, 324 credential-shaped synthetic strings in 123 prompts were replaced with deterministic nonfunctional placeholders. IDs and gold labels did not change, but those 123 public prompts were not re-inferred. The reported metrics are therefore exact for the pre-publication 50,000-row source, while 49,877 public rows remain message-identical. Both input hashes and this scope limitation are recorded in the evidence.

	The public evidence records the served name `drift-sentry-4b-v1`. Internal endpoint URLs, host identifiers, and absolute filesystem paths are retained only in the private evidence archive.

	### Dataset-quality audit

	On `149,515` semantic-corpus rows that retained both an assigned target tier
	and the teacher's unmodified original tier, target-versus-teacher Cohen's kappa
	was `0.9781300729494184` (`146,769` agreements, `2,746` disagreements).
	The `459` rows without a valid raw teacher tier were excluded rather than
	imputed. The exact method and confusion matrix are in
	`evaluation/label_consistency_audit.json`. This is not a human
	inter-annotator-agreement claim.

	### Operational latency

	On one NVIDIA H200 with vLLM, batch 1, concurrency 1, a 2,048-token rendered prompt, and `max_tokens=512`, 1,000 measured requests succeeded with no failures. Natural completion length was 166 tokens and p95 latency was `664.2498452 ms`. This is a capped-output production measurement, not a forced 512-token stress test.

	## Serving with vLLM

	```bash
	vllm serve Tynapse/drift-sentry-4b-v1 \
	--served-model-name drift-sentry-4b-v1 \
	--dtype bfloat16 \
	--max-model-len 4096 \
	--gdn-prefill-backend triton
	```

	Send the policy or taxonomy instructions as a system message, followed by the agent context and candidate response as user content. Use temperature 0 and a JSON response format where supported.

	## Using the LoRA adapter

	The unmerged adapter is stored under `adapter/`. Its `adapter_config.json` pins the same base revision shown above. The merged root model and adapter represent the same selected checkpoint.

	## Training data and method

	The student was trained on synthetic behavioral-safety conversations produced by the single teacher `Qwen/Qwen3.6-27B` at revision `6a9e13bd6fc8f0983b9b99948120bc37f49c13e9` and labeled using the DriftSentry taxonomy. Training, validation, and semantic-test archetype families were separated. The public 50,000-row evaluation benchmark was not used as model training data.

	This release does not claim multi-teacher cross-tokenizer or on-policy logit distillation.

	## Limitations and responsible use

	- Labels are synthetic teacher labels, not a fully human-adjudicated gold standard.
	- The benchmark is intended for controlled behavioral-safety evaluation; it is not evidence of universal robustness across agents, domains, languages, or tools.
	- The model can make false-positive and false-negative decisions. Do not use it as the sole enforcement mechanism for high-impact decisions.
	- Safety examples may contain harmful, deceptive, privacy-sensitive, or policy-violating scenarios. Treat inputs and outputs as sensitive research content.
	- A production integration should validate JSON parsing, retain audit logs, and define a safe fallback for malformed or uncertain outputs.

	## Acknowledgement

	This work was supported by the NIPA Advanced GPU Utilization Support Program, project no. `04-26-03-0029`.

	## Files

	- `model.safetensors`: merged model
	- `adapter/`: original LoRA adapter and configuration
	- `taxonomy.yaml`: six-tier taxonomy and output schema
	- `evaluation/`: exact metrics, provenance, and H200 operational-latency evidence
	- `release_manifest.json`: model-file hashes and base revision