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README.md

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+---
+license: apache-2.0
+library_name: executorch
+tags:
+- android
+- ios
+- on-device
+- pytorch
+- react-native
+- smollm
+- llama
+base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
+---
+
+# SmolLM2-1.7B-Executorch-Q8DA4W
+
+This repository contains the `smollm2_1_7b_q8da4w.pte` model, exported for use with [ExecuTorch](https://pytorch.org/executorch).
+
+## Details
+- **Base Model**: [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
+- **Format**: `.pte` (ExecuTorch)
+- **Quantization**: Q8DA4W (4-bit linear weights, 8-bit dynamic activations)
+- **Architecture**: llama (compatible with Llama export pipeline)
+- **File Size**: ~1.7 GB
+
+## Features
+- 🚀 Optimized for mobile/edge devices
+- 📱 Compatible with `react-native-executorch`
+- 💡 SmolLM2 is efficient and fast for resource-constrained environments
+- 🗣️ Instruct-tuned for conversational AI
+
+## Usage
+This model is ready to be used in mobile applications (iOS/Android) via the ExecuTorch runtime or `react-native-executorch`.
+
+1. Download `smollm2_1_7b_q8da4w.pte` and the tokenizer files (`tokenizer.json`, `vocab.json`, `merges.txt`).
+2. Place them in your app's asset folder.
+3. Load with ExecuTorch runtime.
+
+## Notes
+- SmolLM2 uses **byte-level BPE tokenizer** (similar to GPT-2), not SentencePiece like Llama.
+- Tokenizer files are: `tokenizer.json`, `vocab.json`, `merges.txt`