OpenELM-3B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3.04B |
| License | Apple-ASCL |
| Author | Apple (Original), SanctumAI (GGUF) |
| Training Data | 1.8T tokens (RefinedWeb, PILE, RedPajama, Dolma) |
What is OpenELM-3B-Instruct-GGUF?
OpenELM-3B-Instruct-GGUF is a quantized version of Apple's efficient language model that implements an innovative layer-wise scaling strategy. This model represents a significant advancement in efficient parameter allocation within transformer layers, offering multiple quantization options from 1.15GB to 6.07GB to suit various hardware configurations.
Implementation Details
The model utilizes the CoreNet library for pre-training and features a sophisticated architecture optimized for both performance and efficiency. Available in various GGUF quantizations (Q2_K through Q8_0), it enables deployment across different computational resources while maintaining performance.
- Multiple quantization options with RAM requirements ranging from 3.14GB to 7.72GB
- Implements Zephyr-style prompt template for instruction following
- Trained on a diverse 1.8T token dataset
- Layer-wise scaling for optimal parameter distribution
Core Capabilities
- Instruction following and task completion
- Efficient parameter utilization through layer-wise scaling
- Flexible deployment options through various quantization levels
- Optimized for both performance and resource efficiency
Frequently Asked Questions
Q: What makes this model unique?
Its layer-wise scaling strategy and efficient parameter allocation make it particularly effective for its size class, while multiple quantization options provide exceptional deployment flexibility.
Q: What are the recommended use cases?
The model is well-suited for instruction-following tasks, general language understanding, and applications requiring efficient deployment with limited computational resources.
