nickoo004/queryshield-1.5b

QueryShield — Multilingual Prompt Optimizer

QueryShield-1.5B is a fine-tuned version of 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
Training rows	19,530
Epochs	3
Train loss	0.88 → 0.47
Eval loss	0.967 (best checkpoint)
GPU	NVIDIA RTX 3090 24GB
Training time	~3.7 hours
Parameters	1.5B total / 147M trainable (8.7%)
Live demo	▶ Kaggle Notebook

---

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

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)

---

Live Demo

▶ Run on Kaggle — no setup needed, free GPU included.

Tests all 7 cases: English, Uzbek, Russian, Kazakh, Karakalpak + 2 cross-lingual pairs.

---

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
• 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  <- best checkpoint

---

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

@model{queryshield_1_5b_2026,
  author    = {nickoo004},
  title     = {QueryShield-1.5B: Multilingual Prompt Optimizer},
  year      = {2026},
  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