llama2-embedding-1b-8k
| Property | Value |
|---|---|
| Model Size | 1B parameters |
| Training Context Length | 8k tokens |
| Max Inference Context | 32k tokens |
| Output Dimension | 1536 |
| Source | Hugging Face |
What is llama2-embedding-1b-8k?
llama2-embedding-1b-8k is a specialized embedding model based on Llama2 architecture, specifically trained for Malaysian text processing. It's designed to generate high-quality vector representations of text with support for extended context lengths, making it particularly valuable for Malaysian language understanding tasks.
Implementation Details
The model leverages the Llama2 architecture with 1 billion parameters, trained on Malaysian text data. While trained on 8k context length, it demonstrates the capability to scale up to 32k tokens during inference, offering flexibility for various application scenarios. The model outputs 1536-dimensional embeddings, suitable for various downstream tasks.
- Built on Llama2 architecture with 1B parameters
- Supports context length scaling from 8k to 32k tokens
- Generates 1536-dimensional embeddings
- Optimized for Malaysian language processing
Core Capabilities
- Text embedding generation for Malaysian content
- Semantic similarity computation
- Support for long-context processing
- Integration with standard transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specific optimization for Malaysian text processing while leveraging the powerful Llama2 architecture. The ability to scale from 8k to 32k context length provides exceptional flexibility for various applications.
Q: What are the recommended use cases?
The model is ideal for tasks involving Malaysian text analysis, including semantic search, content similarity matching, and document clustering. It's particularly useful when dealing with longer text sequences due to its extended context length support.
