ISTA-MLCV/Qwen2.5-7B_single_emb
ISTA-MLCV/Qwen2.5-7B_single_emb is a 7.6 billion parameter Qwen 2.5 model, fine-tuned as a vanilla baseline for the ASIDE research paper. This model serves as an unmodified reference point for architectural separation studies in language models. It is specifically designed for research and evaluation within the context of the ASIDE framework, providing a standard for comparison against modified architectures.
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Model Overview
This model, ISTA-MLCV/Qwen2.5-7B_single_emb, is a 7.6 billion parameter variant of the Qwen 2.5 7B architecture. It has been fine-tuned as a vanilla (unmodified) baseline for the research presented in the paper "ASIDE: Architectural Separation of Instructions and Data in Language Models" by Zverev et al. The primary purpose of this model is to provide a standard, un-modified reference point for comparative studies within the ASIDE framework, allowing researchers to evaluate the impact of architectural changes.
Key Characteristics
- Vanilla Baseline: Represents the standard Qwen 2.5 7B model without any embedding modifications, serving as a control in architectural research.
- Research Context: Developed specifically for the ASIDE paper, focusing on the separation of instructions and data in language models.
- Parameter Count: Features 7.6 billion parameters, offering a substantial foundation for language understanding and generation tasks.
- Context Length: Supports a context length of 32768 tokens.
Intended Use Cases
- Academic Research: Ideal for researchers studying language model architectures, particularly those interested in the ASIDE framework.
- Comparative Analysis: Serves as a crucial baseline for comparing the performance of models with modified embeddings or architectural changes.
- Experimental Control: Provides a stable and unmodified reference for experiments on instruction and data separation in LLMs.