Remostart/Plutus_Advanced_model
The Remostart/Plutus_Advanced_model is a 1.5 billion parameter language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. With a substantial context length of 131072 tokens, it is designed for advanced language understanding and generation tasks. This model leverages its Qwen2.5 base for robust performance in various applications requiring extensive contextual processing.
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Plutus_Advanced_model Overview
The Remostart/Plutus_Advanced_model is a 1.5 billion parameter language model, building upon the robust architecture of Qwen/Qwen2.5-1.5B-Instruct. It features an exceptionally large context window of 131072 tokens, enabling it to process and understand very long sequences of text.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen2.5-1.5B-Instruct, inheriting its foundational capabilities.
- Parameter Count: A compact 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Extended Context Length: A significant 131072-token context window, ideal for tasks requiring deep contextual understanding across extensive documents or conversations.
Training Details
The model was fine-tuned over 2 epochs with a learning rate of 1e-05, using AdamW_Torch_Fused optimizer. Training involved a total batch size of 8, with gradient accumulation steps of 8. The training procedure utilized Transformers 4.57.3 and Pytorch 2.9.0+cu126.
Potential Use Cases
Given its large context window and instruction-tuned base, this model is potentially well-suited for:
- Long-form content analysis: Summarizing, extracting information, or answering questions from very long documents.
- Complex dialogue systems: Maintaining coherence and context over extended conversations.
- Code analysis: Processing large codebases for understanding, debugging, or generation tasks.