Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 0.49B (0.36B Non-Embedding) |
| Model Type | Causal Language Model |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Context Length | 32,768 tokens |
| Repository | Hugging Face |
What is Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit?
This is a highly optimized 4-bit quantized version of the Qwen2.5 0.5B model, implemented using Unsloth's Dynamic 4-bit Quantization technology. It represents a significant advancement in efficient model deployment, offering dramatic reductions in memory usage while maintaining model performance.
Implementation Details
The model features a sophisticated architecture with 24 layers and a unique attention head configuration of 14 heads for queries and 2 for key-values (GQA). It utilizes advanced components like RoPE (Rotary Position Embedding), SwiGLU activation, and RMSNorm, along with attention QKV bias and tied word embeddings.
- Selective 4-bit quantization for optimal accuracy-efficiency trade-off
- 70% reduced memory footprint compared to full precision
- 2-5x faster finetuning capabilities
- Full 32,768 token context length support
Core Capabilities
- Multilingual support for 29+ languages
- Enhanced instruction following abilities
- Improved structured data handling
- Efficient long-text generation (up to 8K tokens)
- JSON and structured output generation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its implementation of Unsloth's Dynamic 4-bit Quantization, which provides significant memory savings and performance improvements while maintaining model quality through selective quantization techniques.
Q: What are the recommended use cases?
While the base model isn't recommended for direct conversations, it's ideal for further fine-tuning through SFT, RLHF, or continued pretraining. It's particularly well-suited for applications requiring efficient deployment with limited computational resources.
