Instructions to use josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy with Transformers:

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

pipe = pipeline("text-generation", model="josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy")
model = AutoModelForCausalLM.from_pretrained("josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy")
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 josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy

SGLang

How to use josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy 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 "josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy" \
    --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": "josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy",
		"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 "josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy" \
        --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": "josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy with Docker Model Runner:
```
docker model run hf.co/josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy
```

qwen2.5-7b-prt-hipaa-structured-full-policy

Qwen2.5-7B-Instruct fine-tuned on Policy Reasoning Traces (PRTs) for binary compliance assessment, as part of an EMNLP 2026 study.

Train domain: hipaa — structured opus-authored PRTs (format isolated).
Cross-domain holdout: evaluate on the OTHER domain's held-out test set.
Prompt format: no-policy zero-shot chain-of-thought; the model reasons step-by-step and ends with Final Verdict: COMPLIANT or Final Verdict: NONCOMPLIANT.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy")
model = AutoModelForCausalLM.from_pretrained("josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy", torch_dtype="bfloat16", device_map="auto")
msgs = [{"role": "user", "content": PROMPT}]  # see prepare_data.build_prompt

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for josephimperial/qwen2.5-7b-prt-hipaa-structured-full-policy

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Qwen/Qwen2.5-7B

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Qwen/Qwen2.5-7B-Instruct

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