🌐 Svarix-1-1B-Hi

A Hindi-Optimized Fine-Tuned Variant of Gemma-3-1B-IT

By Pathania Svarix AI — India 🇮🇳

---

🔷 Overview

Svarix-1-1B-Hi is a fine-tuned model based on google/gemma-3-1b-it, optimized specifically for natural, conversational, Indian-style Hindi.

Where the base Gemma model sounds formal or machine-translated in Hindi, Svarix-1-1B-Hi has been tuned to produce:

• Everyday conversational Hindi
• Semi-formal/office Hindi
• WhatsApp-style replies
• Short, crisp Indian text responses
• Hindi summaries and translations

This release (v1) focuses on pure Hindi optimization.
Support for Hinglish & Roman Hindi is planned for the next update (v1.1).

---

🧠 Model Description

Field	Value
Base Model	google/gemma-3-1b-it
Architecture	Decoder-only Transformer
Size	1 Billion parameters
Format	safetensors
Precision	FP16
Optimized For	Hindi conversational tasks
Organization	Pathania Svarix Pvt. Ltd.

---

🛠️ Training Details

Fine-tuning Objective

The goal was to make Gemma-3-1B sound more:

• Indian
• Context-aware
• Conversational
• Less formal
• More culturally aligned

Dataset Used

• ai4bharat/sangraha (Hindi portion)
  A curated Hindi corpus containing diverse textual styles:
  • news
  • social text
  • government docs
  • conversational lines
  • general Hindi usage

Training Method

• LoRA / QLoRA-based parameter-efficient tuning
• Larger sequence lengths to reduce training time
• Instruction-style formatting matching Gemma chat template

Full fine-tuning logs and training code will be released in future updates.

---

🎯 Intended Use

This model is ideal for:

• Hindi Chat Assistants
• Customer support bots
• WhatsApp/TG/website chat widgets
• Indian small-scale AI workflows
• Basic Hindi summarization & paraphrasing
• Hindi-English translation (simple level)
• Document reply generation
• Call center or support desk tools

Good for

• < 8 GB GPU setups
• Mobile or edge devices (quantized versions coming soon)

---

🚫 Not Intended For

• Legal or medical advice
• Financial or compliance-critical answers
• Deep reasoning tasks
• High-risk or safety-critical systems
• Misinformation generation
• Political persuasion

---

📦 How to Use

Python Example

from transformers import AutoTokenizer, AutoModelForCausalLM

model = "PathaniaSvarix/Svarix-1-1b-hi"

tok = AutoTokenizer.from_pretrained(model)
mod = AutoModelForCausalLM.from_pretrained(model)

inp = "एक छोटा सा हिंदी सारांश लिखो: भारत का बजट 2024 क्या कहता है?"
x = tok(inp, return_tensors="pt")

out = mod.generate(
    **x,
    max_new_tokens=200,
    temperature=0.7
)

print(tok.decode(out[0], skip_special_tokens=True))

---

🗣️ Example Prompts

Casual Hindi

आज मौसम कैसा रहेगा?

Office Hindi

कृपया इस मेल का एक छोटा पेशेवर उत्तर लिखें।

Summarization

इस पैराग्राफ का 3 लाइन का सारांश लिखो।

Translation

Translate to Hindi: "Please complete the form and submit before 5 pm."

---

🔄 Roadmap

Planned for v1.1 (Next Release)

• Hinglish optimization
• Roman Hindi training
• Improved chat alignment
• GGUF quantized models for LM Studio / Ollama
• Full training scripts
• Evaluation benchmarks
• HF Space demo (public playground UI)

---

🛡️ License

This model is a derivative of
google/gemma-3-1b-it
and is therefore distributed under the same Gemma License Terms.

License: Gemma Terms of Use
Full terms: https://ai.google.dev/gemma/terms

You must comply with the original Gemma license when using or redistributing this model.

---

🏢 Organization

Developed by:
Pathania Svarix Pvt. Ltd.
Svarix AI Division — India 🇮🇳

Contact (AI Wing): contact@svarix.ai

---

⭐ Acknowledgements

Special thanks to:

• Google DeepMind — for the Gemma architecture
• AI4Bharat — for the Sangraha Hindi datasets
• Open-source community

---

🔔 Notes

This is v1 of the model.
Future versions will significantly improve:

• Hinglish
• Roman Hindi
• General reasoning ability
• Safety alignment

Stay tuned for updates.