rbelanec/train_sst2_42_1776331411
The rbelanec/train_sst2_42_1776331411 model is a 1 billion parameter instruction-tuned language model, fine-tuned by rbelanec from the meta-llama/Llama-3.2-1B-Instruct base model. It is specifically optimized for sentiment analysis tasks, demonstrating a validation loss of 0.0976 on the SST-2 dataset. This model is designed for efficient deployment in applications requiring sentiment classification, leveraging its compact size and specialized training.
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Model Overview
This model, rbelanec/train_sst2_42_1776331411, is a 1 billion parameter language model fine-tuned from the meta-llama/Llama-3.2-1B-Instruct base. It has been specialized for sentiment analysis, specifically on the SST-2 dataset, achieving a validation loss of 0.0976.
Key Capabilities
- Sentiment Analysis: Optimized for binary sentiment classification tasks, as evidenced by its training on the SST-2 dataset.
- Efficient Inference: With 1 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for resource-constrained environments.
- Instruction Following: Inherits instruction-following capabilities from its Llama-3.2-1B-Instruct base, adapted for sentiment-related instructions.
Training Details
The model was trained with a learning rate of 5e-06, a batch size of 8, and 5 epochs. The training utilized an AdamW optimizer with a cosine learning rate scheduler and a warmup ratio of 0.1. The training process involved processing over 18 million input tokens, leading to the reported validation loss.
When to Use This Model
This model is particularly well-suited for applications requiring fast and accurate sentiment classification, especially where the input text is similar in nature to the SST-2 dataset. Its smaller size compared to larger LLMs makes it a good choice for edge devices or applications with strict latency requirements.