This model is currently on research and experiment, waiting for the official name and further information
Lightweight, text-only models that fit onto edge and mobile devices, finetuned version of Llama3.2 with WangchanThaiInstruct Dataset (only Financial and Retail tags)
Support context length of 128K tokens and are state-of-the-art in their class for on-device use cases like summarization, instruction following, and rewriting tasks running locally at the edge.
The Model empower developers to build personalized, on-device agentic applications with strong privacy where data never leaves the device.
Outperforms the Gemma 2 2B and Phi 3.5-mini (3.8B) models on tasks such as following instructions, summarization, prompt rewriting, and tool-use, while the 1B is competitive with Gemma.
Multilingual Support in one single model. (Including Thai Language)
Enhance more model capabilities by using RAG (Retrieval Augmented Generation) + Responsible AI supported
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Model tree for boatchrnthn/BoatSLMPrototype_3B
Base model
meta-llama/Llama-3.2-3B-Instruct