Jacqkues/Qwen2.5-Coder-3B-Instruct-abliterated
Jacqkues/Qwen2.5-Coder-3B-Instruct-abliterated is a 3.1 billion parameter instruction-tuned language model based on the Qwen2.5 architecture, developed by Jacqkues. This model is designed for general instruction following, leveraging its compact size and 32K context length for efficient deployment. It aims to provide a capable foundation for various natural language processing tasks, particularly where resource constraints are a consideration.
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Model Overview
Jacqkues/Qwen2.5-Coder-3B-Instruct-abliterated is an instruction-tuned language model with 3.1 billion parameters, built upon the Qwen2.5 architecture. This model is designed to follow instructions effectively, making it suitable for a range of natural language processing applications.
Key Characteristics
- Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context window of 32,768 tokens, allowing for processing longer inputs and maintaining conversational coherence.
- Instruction Following: Fine-tuned to understand and execute user instructions, enhancing its utility for interactive and task-oriented applications.
Use Cases
Given the limited information in the provided model card, the model's primary utility is inferred to be general instruction-following tasks. Developers might consider this model for:
- Text Generation: Creating coherent and contextually relevant text based on prompts.
- Question Answering: Responding to queries by extracting or generating information.
- Summarization: Condensing longer texts into shorter, informative summaries.
- Prototyping: As a lightweight yet capable model for initial development and testing of AI applications where larger models might be overkill or too resource-intensive.