yuvraaj23/citynexus-planner-qwen2.5-0.5b
The yuvraaj23/citynexus-planner-qwen2.5-0.5b is a 0.5 billion parameter Qwen2.5-based instruction-tuned causal language model developed by yuvraaj23. It was fine-tuned from unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for tasks requiring efficient processing within a 32768 token context length.
Loading preview...
Model Overview
The yuvraaj23/citynexus-planner-qwen2.5-0.5b is a compact 0.5 billion parameter language model, fine-tuned by yuvraaj23. It is based on the Qwen2.5 architecture and was specifically instruction-tuned from the unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Qwen2.5-based, a causal language model.
- Parameter Count: 0.5 billion parameters, making it suitable for resource-constrained environments or applications requiring faster inference.
- Context Length: Supports a substantial context window of 32768 tokens, allowing it to process longer inputs and maintain conversational coherence over extended interactions.
- Training Efficiency: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
Use Cases
This model is well-suited for applications where a balance between performance and computational efficiency is crucial. Its instruction-tuned nature and large context window make it effective for:
- Text generation: Creating coherent and contextually relevant text.
- Instruction following: Responding to specific prompts and commands.
- Summarization: Condensing longer texts while retaining key information.
- Conversational AI: Engaging in extended dialogues due to its 32768 token context length.