Instructions to use ApiFort/LLMFort-pii with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ApiFort/LLMFort-pii with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(base_model, "ApiFort/LLMFort-pii")

Transformers

How to use ApiFort/LLMFort-pii with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ApiFort/LLMFort-pii", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ApiFort/LLMFort-pii with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ApiFort/LLMFort-pii

SGLang

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

Docker Model Runner
How to use ApiFort/LLMFort-pii with Docker Model Runner:
```
docker model run hf.co/ApiFort/LLMFort-pii
```

LLMFort-pii

File size: 910 Bytes

4cdfb62

{
  "model_id": "Qwen/Qwen3-4B-Instruct-2507",
  "dataset_dir": "/content/drive/MyDrive/pii_v10_training/processed_v10",
  "run_dir": "/content/drive/MyDrive/pii_v10_training/runs/pii_v10_Qwen__Qwen3-4B-Instruct-2507_qlora_20260614_123600",
  "max_seq_length": 2048,
  "max_new_tokens": 384,
  "fixed_eval_size": 100,
  "gold_clean_eval_limit": 200,
  "source_stress_eval_limit": 200,
  "num_train_epochs": 1.0,
  "learning_rate": 0.0001,
  "lora_r": 32,
  "lora_alpha": 64,
  "lora_dropout": 0.05,
  "per_device_train_batch_size": 4,
  "gradient_accumulation_steps": 4,
  "seed": 42,
  "auto_disconnect_runtime": true,
  "run_gradient_sanity_check": true,
  "run_mini_overfit": true,
  "mini_overfit_sample_size": 20,
  "mini_overfit_max_steps": 80,
  "mini_overfit_min_strict_schema": 0.9,
  "mini_overfit_min_exact_match": 0.8,
  "all_label_prompt_ratio": 0.25,
  "adapter_effect_raw_same_alarm_rate": 0.8
}