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Model: Sashvat/HQQ-270M Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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base_model:
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- google/gemma-3-270m-it
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library_name: transformers
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tags:
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- NukeverseAi
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- HQQ
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- HQQ-270M
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- HQQ_270M
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- DeepResearch
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- gemma3
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- gpt_oss
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pipeline_tag: text-generation
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license: other
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---
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# 🚀 Introducing : HQQ-270M
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## Overview :-
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**HQQ-270M** model is developed by **Sashvat AI** by finetuning [Gemma-3](https://huggingface.co/google/gemma-3-270m-it)
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It specializes in **transforming complex, multi-layered user queries into optimized, high-quality Google search queries** .
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⚠️ **Usage Requirement :**
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All input queries **must begin with the prefix `HQQ:`** ( short for **High Quality Query** ) .
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This ensures the model knows the input is intended for query optimization .
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---
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## 🔍 What does it do?
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- Converts **Deep research prompts** into precise search queries.
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- Handles **Broad or ambiguous questions** by breaking them into focused, search-ready chunks.
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- Enhances **information retrieval** by optimizing queries for search engines.
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This model is ideal for :
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- Researchers
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- Students
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- Analysts
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- Anyone needing **faster + higher-quality search results** .
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---
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## ✨ Key Features :
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- **Fine-tuned from Gemma-3** → retains strong language reasoning .
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- **Fast & Efficient** → Gemma's architecture is designed to make the model fast & efficient .
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- **Optimized for real-world queries** → search queries are short, relevant, and actionable.
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- **Prefix-activated (`HQQ:`)** → ensures model is used for its intended purpose.
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---
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## 📦 How to Use
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### 🔧 Installation
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```bash
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pip install transformers accelerate huggingface_hub
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```
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### 🖥️ Inference
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``` python
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Sashvat/HQQ-270M")
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model = AutoModelForCausalLM.from_pretrained("Sashvat/HQQ-270M")
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system_prompt = """
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Convert text after "HQQ: " into an optimized Google search query. Extract key terms, remove filler words, focus on searchable keywords.
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"""
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query = "HQQ: What are the economic, political, and environmental implications of large-scale adoption of nuclear fusion by 2050?"
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messages = [
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{"role": "system", "content": system_prompt },
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{"role": "user", "content": query }
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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```
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---
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## 🚀 Example Output :-
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**Input:**
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> `HQQ: What are the economic, political, and environmental implications of large-scale adoption of nuclear fusion by 2050?`
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**Output :**
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> `"economic political environmental implications" "large-scale adoption nuclear fusion" 2050`
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---
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### Training Loss vs Steps :-
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`Note` : **500 Steps / ~6 Epochs**
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<img src="https://huggingface.co/Sashvat/HQQ-270M/resolve/main/Metric.png">
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---
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## 📊 Intended Use :-
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This model is intended for :
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- Query optimization for **Google search and other search engines**
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- **Information retrieval pipelines** .
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- Assisting **deep research tasks** .
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⚠️ **Important :** Input must **always** begin with `HQQ:` . Without this prefix , results may be unpredictable .
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---
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## 📜 License
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This model is released under the **Sashvat AI License v1.0**.
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You may freely use, modify, and distribute this model, including for commercial purposes .
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However, any use **must clearly state** :
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**"Made by Sashvat AI"**
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📄 Full license: [LICENSE](https://huggingface.co/Sashvat/HQQ-270M/blob/main/LICENSE)
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---
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## 🏢 About Sashvat AI
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We are **Sashvat AI**, from BHARAT 🕉️ . building next-generation productivity tools, AI agents, and research accelerators .
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---
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## 📌 Citation
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If you use this model, please cite :
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```
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@misc{2025-SashvatAI-HQQ-270M,
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title = {HQQ-270M},
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author = {SashvatAI},
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year = {2025},
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url = {https://huggingface.co/Sashvat/HQQ-270M}
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}
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```
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---
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