Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+tags:
+- agriculture
+- farmer-advisory
+- on-device
+- edge-ai
+- LiteRT-LM
+- LiteRT
+- text-generation
+- qwen
+- LoRA
+- Indian-agriculture
+base_model:
+- unsloth/Qwen2.5-1.5B-Instruct
+inference: false
+library_name: litert-lm
+pipeline_tag: text-generation
+---
+
+# Qwen-2.5-1.5B-KCC-LiteRT-LM
+
+This is an on-device farmer advisory language model fine-tuned on cleaned Kisan Call Centre (KCC) question–answer pairs from Indian smallholder farmers, then converted and packaged for efficient offline inference using Google's LiteRT / LiteRT-LM stack.
+
+It is intended for low-connectivity, edge scenarios — such as mobile advisory apps for Indian farmers.
+
+## Model Details
+
+- **Base model**: [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct)
+- **Fine-tuning method**: LoRA (parameter-efficient) via Unsloth + TRL SFTTrainer
+- **Dataset**: Cleaned ["Farmers Call Query Data"](https://www.kaggle.com/datasets/daskoushik/farmers-call-query-data-qa) by Das Koushik, based on data from [data.gov.in](https://www.data.gov.in/)
+  → Only null/empty rows removed; no synthetic data, paraphrasing, or external augmentation
+- **Training regime**: Very short, step-limited runs (max_steps=60, warmup_steps=5) on Colab Tesla T4 due to free-tier constraints  
+  → Pilot for pipeline validation, **not** full convergence
+- **Conversion**: PyTorch → LiteRT (.tflite) using [Google AI Edge Torch](https://github.com/google-ai-edge/ai-edge-torch) (v0.7.1)  
+  → Static KV cache: 4096 tokens  
+  → Result: ~1.6 GB .tflite artifact
+- **Packaging**: .tflite → .litertlm using [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM) (v0.8.1) tools
+- **Quantization**: Quantized graph as produced by AI Edge Torch conversion
+- **Context length**: 4096 tokens (fixed/static KV cache)
+- **Intended use**: Offline, interactive agricultural advisory in low-resource settings
+- **Out-of-scope**: General-purpose chat, high-precision agronomy, multi-turn memory beyond context limit, production-grade fluency
+
+## Performance on Consumer Hardware
+
+Tested on Mac mini (Apple M4, 16 GB unified memory) using LiteRT-LM with GPU backend:
+
+- **Time-to-first-token (TTFT)**: < 1 second
+- **End-to-end response time** (50–150 token advisory answers): ~2.5–4 seconds
+- **Throughput**: Stable incremental decoding
+
+Suitable for real-time, offline farmer-facing tools.
+
+## Important Limitations & Known Behaviors
+
+**This is an early engineering validation release — not a production model.**
+
+Due to extremely short training (Colab constraints):
+
+- Strong mirroring of original KCC terse, bullet-list style → outputs often lack natural conversational flow
+- Occasional near-verbatim reuse of training phrases with limited adaptation to query variations
+- Mild repetition / incomplete reasoning (undertraining artifact)
+
+**LiteRT-LM specific observations** (compared to PyTorch inference):
+
+- Noticeably reduced coherence
+- Increased repetition, fragmentation, or looping in some generations
+- Responses sometimes feel more generic / less tightly grounded
+
+→ These are runtime-specific behaviors (not present in original PyTorch checkpoint).  
+Root cause not yet isolated due to lack of controlled ablation; likely contributors include decoding configuration, stop-token alignment, fixed KV-cache constraints, and runtime-specific sampling behavior.
+
+The project deliberately prioritizes **reproducible deployment path + failure mode transparency** over peak quality. Full multi-epoch training + runtime debugging expected to improve results significantly.
+
+## Comparison with Prior Gemma-3n Effort
+
+Compared to earlier Gemma-3n-E2B fine-tuning on the same task:
+
+- **Qwen2.5-1.5B** wins on: conversion success, long-context stability, deployment reliability
+- **Gemma-3n-E2B** wins on: more natural dialogue style, broader multilingual starting point
+- Deciding factor: Gemma-3n could not be reliably converted to LiteRT-LM with public tooling → hard dead-end
+
+See full comparison in the project documentation.
+
+## Usage
+
+This model is packaged in **.litertlm** format for use with **LiteRT-LM** runtime (preview stage as of Dec 2025).
+
+Refer to:
+
+- [LiteRT-LM GitHub](https://github.com/google-ai-edge/LiteRT-LM)
+- Google AI Edge Gallery app (Android) for quick testing
+- LiteRT documentation for integration into Android/iOS/macOS/Linux apps
+- [UAI.LiteRTLM](https://github.com/Uralstech/UAI.LiteRTLM) — a Unity package wrapping LiteRT-LM inference, useful for building Android/Quest apps with this model
+
+## Reproducibility
+
+Full pipeline (data cleaning → LoRA fine-tuning → merge → LiteRT conversion → LiteRT-LM packaging) is documented with scripts and exact commands in the associated repository.
+
+Conversion requires high-RAM CPU instance (~128 GB recommended). No GPUs needed for conversion/packaging.
+
+See the project documentation for step-by-step instructions (AWS EC2 r6i instances used in original work):  
+https://uralstech.github.io/Qwen-KCC-On-Device-Pipeline