martimfasantos/tinyllama-1.1b-sum-simpo_beta1.0_gamma0.8_LR5e-8_3epochs
This model, developed by martimfasantos, is a 1.1 billion parameter TinyLlama variant fine-tuned from martimfasantos/tinyllama-1.1b-sum-sft-full_old. It is optimized for tasks related to summarization, as indicated by its fine-tuning process and name. The model demonstrates a reward accuracy of 0.6229 on its evaluation set, suggesting its capability in generating preferred outputs. Its compact size makes it suitable for applications requiring efficient, smaller-scale language processing.
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Model Overview
This model, tinyllama-1.1b-sum-simpo_beta1.0_gamma0.8_LR5e-8_3epochs, is a 1.1 billion parameter language model developed by martimfasantos. It is a fine-tuned iteration of the martimfasantos/tinyllama-1.1b-sum-sft-full_old model, indicating a specialization in summarization tasks. The fine-tuning process involved specific hyperparameters including a learning rate of 5e-08, a batch size of 8, and training over 2 epochs.
Key Characteristics
- Base Model: TinyLlama architecture.
- Parameter Count: 1.1 billion parameters.
- Context Length: 2048 tokens.
- Evaluation Performance: Achieved a reward accuracy of 0.6229 on its evaluation set, with a final loss of 1.0879.
- Training Details: Utilized Adam optimizer with cosine learning rate scheduler and a warmup ratio of 0.1.
Intended Use Cases
Given its fine-tuning for summarization, this model is likely suitable for:
- Text Summarization: Generating concise summaries from longer texts.
- Resource-Constrained Environments: Its small size (1.1B parameters) makes it efficient for deployment where computational resources are limited.
Limitations
The README indicates that more information is needed regarding its intended uses, limitations, and training/evaluation data. Users should exercise caution and conduct further testing to determine its suitability for specific applications.