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README.md

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+---
+license: mit
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+language:
+  - en
+  - uz
+  - ru
+  - kk
+  - kaa
+tags:
+  - queryshield
+  - prompt-optimization
+  - multilingual
+  - instruction-tuning
+  - lora
+  - qlora
+  - qwen2.5
+  - uzbek
+  - karakalpak
+  - kazakh
+  - central-asia
+  - fine-tuned
+pipeline_tag: text-generation
+datasets:
+- nickoo004/queryshield-multilingual
+---
+
+# QueryShield — Multilingual Prompt Optimizer
+
+**QueryShield-1.5B** is a fine-tuned version of [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) trained to rewrite raw, messy user queries into detailed, structured instruction prompts for downstream LLMs — across 5 languages and 30 professional domains.
+
+> Given a raw user question → outputs an expert-level optimized prompt telling a downstream LLM *how* to answer it.
+
+---
+
+## What it does
+
+Most LLMs perform significantly better when given structured, detailed prompts rather than raw user input. QueryShield sits **between the user and the LLM** — it takes the raw query and rewrites it into a high-quality instruction prompt automatically.
+
+```
+User: "menga diabetni boshqarish uchun ovqat rejimi ayting"
+         ↓  QueryShield
+Optimized: "As a Medical Expert, the user is asking in Uzbek about dietary
+            management for diabetes with high blood sugar. Provide a structured
+            3-tier response covering: diabetes basics, dietary assessment, and
+            an actionable meal plan. Respond entirely in Uzbek. Avoid jargon..."
+         ↓  Downstream LLM
+Final answer in Uzbek ✅
+```
+
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
+| **Training data** | [QueryShield Multilingual Dataset](https://huggingface.co/datasets/nickoo004/queryshield-multilingual) |
+| **Training rows** | 19,530 |
+| **Epochs** | 3  |
+| **Train loss** | 0.88 → 0.47 |
+| **Eval loss** | 0.967 |
+| **GPU** | NVIDIA RTX 3090 24GB |
+| **Training time** | ~3.7 hours |
+| **Parameters** | 1.5B total / 147M trainable (8.7%) |
+
+---
+
+## Languages
+
+| Language | Code | Support |
+|---|---|---|
+| English | `en` | ✅ Full |
+| Uzbek | `uz` | ✅ Full |
+| Russian | `ru` | ✅ Full |
+| Kazakh | `kk` | ✅ Full |
+| Karakalpak | `kaa` | ✅ Good |
+
+**Cross-lingual** scenarios supported — user can write in one language and request output in another (e.g., Uzbek input → Russian output).
+
+---
+
+## Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "nickoo004/queryshield-1.5b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+SYSTEM = (
+    "You are QueryShield, a multilingual prompt optimizer. "
+    "Given a raw user question, rewrite it into a detailed instruction "
+    "prompt for a downstream LLM expert. "
+    "User language: {in_lang}. Response language: {out_lang}. "
+    "Expert role: {role}."
+)
+
+def optimize_prompt(user_question, input_language, output_language, role):
+    messages = [
+        {"role": "system", "content": SYSTEM.format(
+            in_lang=input_language,
+            out_lang=output_language,
+            role=role,
+        )},
+        {"role": "user", "content": user_question},
+    ]
+    text = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            temperature=0.7,
+            do_sample=True,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    new_tokens = output[0][inputs["input_ids"].shape[1]:]
+    return tokenizer.decode(new_tokens, skip_special_tokens=True)
+
+
+# Example 1 — Uzbek monolingual
+result = optimize_prompt(
+    user_question="menga diabetni boshqarish uchun eng yaxshi ovqatlanish rejimini ayting",
+    input_language="Uzbek",
+    output_language="Uzbek",
+    role="Medical Expert",
+)
+print(result)
+
+# Example 2 — Cross-lingual: Kazakh → Uzbek
+result = optimize_prompt(
+    user_question="менің фермамда топырақ сапасы нашар, не істеуім керек?",
+    input_language="Kazakh",
+    output_language="Uzbek",
+    role="Agricultural Scientist",
+)
+print(result)
+```
+
+---
+
+## Supported Domains (30 total)
+
+| Domain | Expert Role |
+|---|---|
+| Software Engineering | Senior Software Engineer |
+| Healthcare & Medicine | Medical Expert |
+| Finance & Banking | Financial Analyst |
+| Legal & Law | Legal Advisor |
+| Data Science & AI | Data Scientist |
+| Cybersecurity | Cybersecurity Specialist |
+| Aviation & Aerospace | Aerospace Engineer |
+| Agriculture | Agricultural Scientist |
+| Education & Teaching | Experienced Educator |
+| Automotive | Automotive Engineer |
+| Pharmaceuticals | Pharmaceutical Researcher |
+| Manufacturing | Manufacturing Expert |
+| Civil / Mechanical / Electrical Engineering | Domain Engineer |
+| Business & Marketing | Business Strategist |
+| Creative Writing | Professional Writer |
+| … and 15 more | … |
+
+---
+
+## Training Details
+
+### Dataset
+- **Source:** [nickoo004/queryshield-multilingual](https://huggingface.co/datasets/nickoo004/queryshield-multilingual)
+- **19,530 rows** across 5 languages and 30 domains
+- Generated by DeepSeek, Gemini, and Qwen2.5-14B
+
+
+### Loss Curve
+```
+Epoch 1.0  →  train: 1.023  |  eval: 0.997
+Epoch 2.5  →  train: 0.731  |  eval: 0.967
+```
+
+---
+
+## Limitations
+
+- Karakalpak support is functional but may be less consistent than other languages due to limited training data for this low-resource language
+- `optimized_prompt` output is always structured as an English instruction — this is by design
+- Best results on domains covered in training data; novel domains may produce generic prompts
+- Not suitable for harmful, illegal, or unethical query optimization
+
+---
+
+## Citation
+
+```bibtex
+@model{queryshield_1_5b_2025,
+  author    = {nickoo004},
+  title     = {QueryShield-1.5B: Multilingual Prompt Optimizer},
+  year      = {2025},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/nickoo004/queryshield-1.5b}
+}
+```
+
+---
+
+## License
+
+This model is released under the **MIT License**.
+Base model license: [Qwen License](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE)