Instructions to use Accuknoxtechnologies/PII-Qwen3.5-2B-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Accuknoxtechnologies/PII-Qwen3.5-2B-v8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Accuknoxtechnologies/PII-Qwen3.5-2B-v8")
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("Accuknoxtechnologies/PII-Qwen3.5-2B-v8")
model = AutoModelForMultimodalLM.from_pretrained("Accuknoxtechnologies/PII-Qwen3.5-2B-v8")
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 Accuknoxtechnologies/PII-Qwen3.5-2B-v8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Accuknoxtechnologies/PII-Qwen3.5-2B-v8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Accuknoxtechnologies/PII-Qwen3.5-2B-v8",
		"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/Accuknoxtechnologies/PII-Qwen3.5-2B-v8

SGLang

How to use Accuknoxtechnologies/PII-Qwen3.5-2B-v8 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 "Accuknoxtechnologies/PII-Qwen3.5-2B-v8" \
    --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": "Accuknoxtechnologies/PII-Qwen3.5-2B-v8",
		"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 "Accuknoxtechnologies/PII-Qwen3.5-2B-v8" \
        --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": "Accuknoxtechnologies/PII-Qwen3.5-2B-v8",
		"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 Accuknoxtechnologies/PII-Qwen3.5-2B-v8 with Docker Model Runner:
```
docker model run hf.co/Accuknoxtechnologies/PII-Qwen3.5-2B-v8
```

Yash1005 commited on 19 days ago

Commit

502ecf7

verified ·

1 Parent(s): 00e9841

update model card

Browse files

Files changed (1) hide show

README.md +57 -1

README.md CHANGED Viewed

@@ -112,5 +112,61 @@ out = model.generate(**tok(text, return_tensors="pt").to(model.device),
 print(tok.decode(out[0], skip_special_tokens=True))
 ```
 ---
-*Card generated at 2026-05-31 07:14 UTC. Adapter weights: `Accuknoxtechnologies/PII-Qwen3.5-2B-v8`.*

 print(tok.decode(out[0], skip_special_tokens=True))
 ```
+## Evaluation — vLLM serving (merged model, text-only)
+Same **200 held-out prompts**, served through **vLLM `0.21.0`** instead of the transformers `.generate()` loop. Greedy decoding, dtype bf16, `enable_prefix_caching=True`, `enable_chunked_prefill=True`. This reflects production serving accuracy + latency.
+- JSON parse errors: `0/200` (`0.0%`)
+### Accuracy (vLLM)
+| Metric | Value |
+|---|---:|
+| `is_valid` accuracy | **1.0000** |
+| category key-set accuracy | **0.9350** |
+| category value-set accuracy | **0.8300** |
+| Binary F1 (positive = contains PII) | **1.0000** |
+| Binary precision | 1.0000 |
+| Binary recall | 1.0000 |
+| Macro F1 (key-presence) | **0.9791** |
+| Macro F1 (value-set) | **0.9529** |
+### Confusion matrix — binary `is_valid` (vLLM)
+| | predicted PII | predicted clean |
+|---|---:|---:|
+| **actual PII** | TP = 177 | FN = 0 |
+| **actual clean** | FP = 0 | TN = 23 |
+### Per-category key-presence (vLLM)
+| Category | Support | Precision | Recall | F1 |
+|---|---:|---:|---:|---:|
+| address | 79 | 0.987 | 0.987 | 0.987 |
+| bank_account | 12 | 1.000 | 1.000 | 1.000 |
+| card_number | 25 | 1.000 | 1.000 | 1.000 |
+| credentials | 10 | 1.000 | 1.000 | 1.000 |
+| date | 95 | 1.000 | 1.000 | 1.000 |
+| drivers_license | 27 | 0.957 | 0.815 | 0.880 |
+| email | 76 | 0.987 | 1.000 | 0.993 |
+| ip_address | 9 | 1.000 | 1.000 | 1.000 |
+| name | 107 | 1.000 | 0.991 | 0.995 |
+| national_id | 52 | 0.911 | 0.981 | 0.944 |
+| passport_number | 21 | 0.955 | 1.000 | 0.977 |
+| phone_number | 63 | 1.000 | 0.984 | 0.992 |
+| tax_id | 24 | 0.920 | 0.958 | 0.939 |
+| username | 9 | 1.000 | 1.000 | 1.000 |
+### vLLM inference latency (single-stream, batch = 1)
+| Stat | ms / prompt |
+|---|---:|
+| Mean | **576.0** |
+| Median | 511.6 |
+| p95 | 1151.7 |
+| p99 | 1440.7 |
+| Max | 3209.3 |
+| Under 1 s | 89.0% |
+### vLLM throughput (single batched submit)
+- Prompts/sec: **27.73**
+- Output tokens/sec: 1569.0
+- Input tokens/sec: 35596.5
+- Batched wall time for all 200 prompts: 7.21 s
 ---
+*Card generated at 2026-05-31 07:39 UTC. Adapter weights: `Accuknoxtechnologies/PII-Qwen3.5-2B-v8`.*