5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.4-sigmoid
The 5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.4-sigmoid is a 3.2 billion parameter language model, fine-tuned from Llama-3.2-3B-Instruct using Direct Preference Optimization (DPO) with a random pruning method. This model is designed for instruction-following tasks, leveraging DPO on preference data to enhance its conversational abilities. It maintains a context length of 32768 tokens, making it suitable for applications requiring processing of longer inputs.
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Model Overview
This model, 5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.4-sigmoid, is a 3.2 billion parameter instruction-tuned language model. It is derived from the Llama-3.2-3B-Instruct base model and has been fine-tuned using Direct Preference Optimization (DPO). A key characteristic of this model is the application of a random pruning method during its training process, which distinguishes its optimization approach.
Key Characteristics
- Base Model: Llama-3.2-3B-Instruct.
- Fine-tuning Method: Direct Preference Optimization (DPO) on preference data.
- Pruning: Incorporates a random pruning method during training.
- Context Length: Supports a substantial context of 32768 tokens.
Usage and Limitations
Developers can integrate this model using the Hugging Face transformers library for various natural language generation tasks. As a pruned model, it inherits the general limitations of its Llama-3.2-3B-Instruct base and may exhibit additional characteristics or limitations stemming from the pruning process itself. Users should consider these factors when deploying the model for specific applications.