Mrigank005/SLM-sentiment-crosslingual-seed-123
Mrigank005/SLM-sentiment-crosslingual-seed-123 is a 3.1 billion parameter Qwen2.5-3B-Instruct model, fine-tuned by Mrigank005. This model was optimized for training speed using Unsloth and Huggingface's TRL library, offering a 32768 token context length. It is designed for tasks related to sentiment analysis, particularly in cross-lingual contexts, leveraging its Qwen2.5 base for robust language understanding.
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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.