Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,56 @@
+---
+language: [en, hi]
+license: gemma
+tags:
+  - function-calling
+  - gemma
+  - kosha
+  - android
+  - on-device
+  - litert
+  - mediapipe
+  - finance
+base_model: 2796gauravc/kosha-functiongemma-phase0
+---
+
+# KOSHA Phase 0 — Android .task File
+
+**KOSHA (कोश)** — Private on-device Indian finance tracker.  
+This is the **MediaPipe-ready `.task` file** for Android LiteRT deployment.
+
+The fine-tuned merged model is at [2796gauravc/kosha-functiongemma-phase0](https://huggingface.co/2796gauravc/kosha-functiongemma-phase0).
+
+## File Info
+| Property | Value |
+|---|---|
+| Base model | FunctionGemma 270M |
+| Quantization | dynamic_int8 |
+| KV cache max len | 1024 |
+| Size | ~290 MB |
+| Format | MediaPipe `.task` (LiteRT) |
+
+## Functions
+- `log_expense` — Indian bills, UPI, groceries, fuel, dining
+- `log_income` — Salary, freelance, UPI received
+- `no_expense` — OTP, promotions, non-financial messages
+
+## Android Integration
+
+```kotlin
+// build.gradle.kts
+implementation("com.google.mediapipe:tasks-genai:0.10.22")
+
+// Usage
+val options = LlmInference.LlmInferenceOptions.builder()
+    .setModelPath("/data/local/tmp/kosha_phase0_q8_ekv1024.task")
+    .setMaxTokens(1024)
+    .setTopK(64)
+    .setTopP(0.95f)
+    .setTemperature(0.1f)   // Low temp for deterministic function calls
+    .setPreferredBackend(LlmInference.Backend.CPU)  // CPU recommended for Gemma 270M
+    .build()
+val llm = LlmInference.createFromOptions(context, options)
+
+val result = llm.generateResponse(yourFunctionGemmaPrompt)
+// Parse: <start_function_call>call:log_expense{...}<end_function_call>
+```