---
language:
- fa
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- persian
- qwen
- qlora
- legal-nlp
- regulatory-qa
- water-regulations
- instruction-tuning
---

# Persian Water Regulations Qwen2.5-1.5B

A Persian domain-adapted language model for answering questions about the
"Operational By-law and General Conditions of Water and Wastewater Tariffs".

## Base model

`Qwen/Qwen2.5-1.5B-Instruct`

## Training data

- Records: 274
- Training records: 246
- Validation records: 28
- Format: conversational prompt-completion JSONL
- Split: group-aware by source section to reduce source leakage

## Training method

- Method: QLoRA / supervised fine-tuning
- Quantization: 4-bit NF4 with double quantization
- LoRA rank: 16
- LoRA alpha: 32
- LoRA dropout: 0.05
- Target modules: `all-linear`
- Learning rate: 1e-04
- Requested epochs: 10
- Maximum sequence length: 512
- GPU: Tesla T4

## Validation generation metrics

### Base model

- Exact match: 0.0000
- Token F1: 0.2044
- ROUGE-L F1: 0.1670

### Fine-tuned model

- Exact match: 0.0000
- Token F1: 0.3623
- ROUGE-L F1: 0.3196

The automatic metrics measure textual similarity and do not establish legal
correctness. Manual review is required for authoritative use.

## Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Hesam760/persian-water-regulations-qwen2.5-1.5b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype=torch.float16,
)

messages = [
    {
        "role": "system",
        "content": (
            "شما دستیار تخصصی آیین‌نامه عملیاتی و شرایط عمومی تعرفه‌های "
            "آب و فاضلاب هستید. فقط بر اساس مفاد آیین‌نامه پاسخ دهید."
        ),
    },
    {
        "role": "user",
        "content": "هدف آیین‌نامه چیست؟",
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

output_ids = model.generate(
    **inputs,
    max_new_tokens=160,
    do_sample=False,
)

new_tokens = output_ids[0, inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))
```

## Intended use

- Demonstration of Persian domain adaptation
- Regulatory question answering
- Educational and research use

## Limitations

- The model is trained on a small, single-document dataset.
- It can reproduce outdated, incomplete, or incorrectly extracted provisions.
- It may hallucinate provisions or section numbers.
- It is not a legal authority and must not be used as a substitute for the
  official regulation or professional advice.
- Verify the redistribution rights of the source-derived training dataset
  before publishing the dataset itself.