---
language:
- en
base_model:
- google/gemma-3-270m-it
library_name: transformers
tags:
- NukeverseAi
- HQQ
- HQQ-270M
- HQQ_270M
- DeepResearch
- gemma3
- gpt_oss
pipeline_tag: text-generation
license: other
---
# 🚀 Introducing : HQQ-270M

## Overview :-

**HQQ-270M** model is developed by **Sashvat AI** by finetuning [Gemma-3](https://huggingface.co/google/gemma-3-270m-it)
It specializes in **transforming complex, multi-layered user queries into optimized, high-quality Google search queries** .

⚠️ **Usage Requirement :**
All input queries **must begin with the prefix `HQQ:`** ( short for **High Quality Query** ) .
This ensures the model knows the input is intended for query optimization .

---

## 🔍 What does it do?

- Converts **Deep research prompts** into precise search queries.  
- Handles **Broad or ambiguous questions** by breaking them into focused, search-ready chunks.  
- Enhances **information retrieval** by optimizing queries for search engines.  

This model is ideal for :
- Researchers  
- Students  
- Analysts  
- Anyone needing **faster + higher-quality search results** .  

---

## ✨ Key Features :

- **Fine-tuned from Gemma-3** → retains strong language reasoning .
- **Fast & Efficient** → Gemma's architecture is designed to make the model fast & efficient . 
- **Optimized for real-world queries** → search queries are short, relevant, and actionable.  
- **Prefix-activated (`HQQ:`)** → ensures model is used for its intended purpose.  

---

## 📦 How to Use

### 🔧 Installation

```bash
pip install transformers accelerate huggingface_hub
```

### 🖥️ Inference

``` python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sashvat/HQQ-270M")
model = AutoModelForCausalLM.from_pretrained("Sashvat/HQQ-270M")

system_prompt = """
Convert text after "HQQ: " into an optimized Google search query. Extract key terms, remove filler words, focus on searchable keywords.
"""

query = "HQQ: What are the economic, political, and environmental implications of large-scale adoption of nuclear fusion by 2050?"

messages = [
    {"role": "system", "content": system_prompt },
    {"role": "user", "content": query }
]

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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
```

---

## 🚀 Example Output :-

**Input:**

> `HQQ: What are the economic, political, and environmental implications of large-scale adoption of nuclear fusion by 2050?`

**Output :**

> `"economic political environmental implications" "large-scale adoption nuclear fusion" 2050`


---

### Training Loss vs Steps :-

`Note` : **500 Steps / ~6 Epochs**

<img src="https://huggingface.co/Sashvat/HQQ-270M/resolve/main/Metric.png">

---

## 📊 Intended Use :-

This model is intended for :

- Query optimization for **Google search and other search engines**
- **Information retrieval pipelines** .
- Assisting **deep research tasks** .

⚠️ **Important :** Input must **always** begin with `HQQ:` . Without this prefix , results may be unpredictable .

---

## 📜 License

This model is released under the **Sashvat AI License v1.0**.  
You may freely use, modify, and distribute this model, including for commercial purposes .  
However, any use **must clearly state** :

**"Made by Sashvat AI"**

📄 Full license: [LICENSE](https://huggingface.co/Sashvat/HQQ-270M/blob/main/LICENSE)

---

## 🏢 About Sashvat AI

We are **Sashvat AI**, from BHARAT 🕉️ . building next-generation productivity tools, AI agents, and research accelerators .

---

## 📌 Citation

If you use this model, please cite :

```
@misc{2025-SashvatAI-HQQ-270M,
  title   = {HQQ-270M},
  author  = {SashvatAI},
  year    = {2025},
  url     = {https://huggingface.co/Sashvat/HQQ-270M}
}
```
---