Model Overview

Mrigank005/SLM-sentiment-crosslingual-seed-123 is a fine-tuned variant of the Qwen2.5-3B-Instruct model, developed by Mrigank005. This 3.1 billion parameter model features a substantial context length of 32768 tokens, making it suitable for processing longer texts. It was specifically optimized for faster training, achieving a 2x speedup by utilizing the Unsloth library in conjunction with Huggingface's TRL library.

Key Capabilities

• Efficient Training: Benefits from Unsloth's optimizations for significantly faster fine-tuning.
• Qwen2.5 Base: Leverages the robust architecture and language understanding capabilities of the Qwen2.5-3B-Instruct foundation model.
• Extended Context: Supports a 32768 token context window, allowing for comprehensive analysis of longer inputs.

Good For

• Sentiment Analysis: The model's name suggests a focus on sentiment tasks.
• Cross-lingual Applications: Implies utility in understanding sentiment across different languages.
• Resource-Efficient Deployment: As a 3.1B parameter model, it offers a balance between performance and computational cost.