Instructions to use imabedalghafer/maqsm_model_pii_removal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use imabedalghafer/maqsm_model_pii_removal with Transformers:

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

pipe = pipeline("text-generation", model="imabedalghafer/maqsm_model_pii_removal")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("imabedalghafer/maqsm_model_pii_removal")
model = AutoModelForCausalLM.from_pretrained("imabedalghafer/maqsm_model_pii_removal", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use imabedalghafer/maqsm_model_pii_removal with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "imabedalghafer/maqsm_model_pii_removal"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "imabedalghafer/maqsm_model_pii_removal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/imabedalghafer/maqsm_model_pii_removal

SGLang

How to use imabedalghafer/maqsm_model_pii_removal 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 "imabedalghafer/maqsm_model_pii_removal" \
    --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": "imabedalghafer/maqsm_model_pii_removal",
		"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 "imabedalghafer/maqsm_model_pii_removal" \
        --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": "imabedalghafer/maqsm_model_pii_removal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use imabedalghafer/maqsm_model_pii_removal with Docker Model Runner:
```
docker model run hf.co/imabedalghafer/maqsm_model_pii_removal
```

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PII removal model

This model is a fine-tuned version of the Qwen 3 model used for submission on the competition hosted by Maqsam, link for here

Model Details

Model Description

This is based on Qwen 3 without reasoning tokens to increase the latency, the default system prompt used in the fine-tuning should be helpful as default message for the model.

Developed by: [TheConsultant Team]
Model type: [Causal LLM]
Language(s) (NLP): [token masking]
License: [MIT]
Finetuned from model [optional]: [Qwen/Qwen3-0.6B]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

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Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for imabedalghafer/maqsm_model_pii_removal

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

(1135)

this model