Text Generation
Transformers
Safetensors
qwen2
compliance
legal
lora
qlora
prt
policy-reasoning-traces
conversational
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("josephimperial/qwen2.5-7b-prt-gdpr-unstructured-full-policy")
model = AutoModelForCausalLM.from_pretrained("josephimperial/qwen2.5-7b-prt-gdpr-unstructured-full-policy", 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]:]))Quick Links
qwen2.5-7b-prt-gdpr-unstructured-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:
gdpr— unstructured 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: COMPLIANTorFinal Verdict: NONCOMPLIANT.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("josephimperial/qwen2.5-7b-prt-gdpr-unstructured-full-policy")
model = AutoModelForCausalLM.from_pretrained("josephimperial/qwen2.5-7b-prt-gdpr-unstructured-full-policy", torch_dtype="bfloat16", device_map="auto")
msgs = [{"role": "user", "content": PROMPT}] # see prepare_data.build_prompt
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="josephimperial/qwen2.5-7b-prt-gdpr-unstructured-full-policy") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)