rishuXori
/

gemma-3-1b-FT

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+license: apache-2.0
+language: ["en", "hi"] # English, Hindi, Hinglish
+tags:
+- text-generation
+- gemma
+- fine-tuned
+- speech-to-text
+- turn-detection
+- conversational-ai
+pipeline_tag: text-generation
+base_model: google/gemma-3-1b-it
 ---
+# rishuXori/gemma-3-1b-FT: The Conversational Flow Maestro 🎤➡️💬
+## Model Description
+Welcome to `rishuXori/gemma-3-1b-FT`, a specialized fine-tuned version of Google's powerful Gemma 2B model. We've taken the robust foundation of Gemma and sculpted it for a unique and critical task in conversational AI: **intelligently detecting when a user's speech is complete, even amidst real-world "noise" and nuances.**
+This model is an **LLM (Large Language Model)** meticulously trained to understand spoken language after it's been processed by a Speech-to-Text (STT) system. Its core superpower? It discerns whether a user has finished their thought, acting as a crucial "turn detector" in dynamic voice bot interactions.
+* **Model Type:** LLM (Large Language Model)
+* **Languages:** English, Hinglish, Hindi (Devanagari script) - *Breaking down language barriers for seamless conversations!*
+* **Finetuned from:** `google/gemma-3-1b-it`
+## Unlocking Natural Conversations: The Power of Turn Detection
+In the world of voice bots and conversational AI, the transition between a user speaking and the bot responding is key to a natural, fluent experience. Awkward interruptions or long silences can quickly lead to user frustration. That's where `rishuXori/gemma-3-1b-FT` shines!
+### Key Use Cases:
+* **Intelligent Turn Detection:** This model is specifically engineered to analyze text output from Speech-to-Text (STT) systems and predict whether the user's message is truly complete. It's designed to handle the messy, "noisy" text that often comes from real-time speech, making it robust in real-world scenarios.
+* **Seamless Voice Bot Interactions:** Imagine a voice bot that knows exactly when to listen and when to speak. This model was fine-tuned to be the **critical "turn detector"** component positioned between your STT and Text-to-Speech (TTS) models in a voice bot setup.
+* **Enhanced User Experience:** By accurately predicting the completion of a message, this model significantly reduces instances of accidental interruptions or the bot speaking over the user, leading to a much smoother, more human-like conversational flow.
+### How it Works:
+The model achieves its precision by looking for an `<end_of_turn>` token (or similar semantic cues) within the incoming message. Its fine-tuning ensures that after processing the message, it generates *only a single token* as its output. This focused generation (by setting `max_tokens=1` during inference) allows for a swift and decisive prediction of whether the user's turn has ended, signaling to the voice bot that it's time to generate its response.
+---