NOTICE: This is an experimental model released for research and development purposes. It serves as a proof-of-concept for a 4-stage post-training pipeline on Small Language Models (SLMs).

Indica-1.7B is a lightweight model developed by Prashant to explore the limits of persona-injection and cultural alignment in ultra-small parameter architectures (1.7B). Built on Qwen3-1.7B, this model was subjected to a rigorous training regime including SFT, GRPO (Reasoning), and DPO (Alignment).

🔬 The Research Objective

The goal of this project was to test whether a 1.7B model could successfully balance three conflicting objectives:

Domain Expertise: Knowledge of Indian Law (IPC/BNS) and Agriculture.
Linguistic Persona: Natural, colloquial Hinglish/Hindi code-switching.
Logic & Reasoning: Utilizing a native "Thinking" trace via Reinforcement Learning.

🛠️ Post-Training Pipeline

The model underwent a specialized four-stage alignment strategy:

Stage 1: SFT (Knowledge): Trained on Indian Law and Agriculture datasets.
Stage 2: GRPO (Reasoning): Reinforcement Learning to reward the use of <think> tags for logical tasks.
Stage 3: DPO (Persona): Preference alignment to craft a friendly "Indian AI Assistant" identity.
Stage 4: Optimization: Exported via Unsloth for high-efficiency inference.

📉 Known Limitations & Experimental Findings (The "Alignment Tax")

As an experimental 1.7B model, Indica demonstrates several critical findings regarding Catastrophic Forgetting:

Factual Regression: Due to the limited parameter capacity, the final alignment (DPO) stage has caused the model to lose some precision in mathematical calculations and specific legal section numbering.
Persona Drift: The model prioritizes its "creative persona" over technical accuracy. It may identify itself as an "AI Zindagi Manager" or other creative identities.
Logic Bypassing: In some instances, the model may skip the internal <think> reasoning trace and provide direct, occasionally incorrect, answers.
Repetition Loops: Occasional gibberish or repetition loops may occur in conversational Hinglish.

📦 Deployment for Testing

This model is best used to study Hinglish conversational patterns or as a base for further fine-tuning experiments.

With Ollama

bash

ollama run hf.co/prash616/Indica-1.7B-GGUF

🤝 Credits & Acknowledgements

Developer: Prashant (prash616)
Base Model: Alibaba Qwen Team
Training Framework: Unsloth AI

Disclaimer

This model is intended solely for educational and research purposes.
It should not be used as a substitute for professional advice, including but not limited to legal, agricultural, or mathematical decision-making.