Hiber-Multi-10B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 10 Billion |
| Architecture | Decoder-only Transformer |
| Context Length | 4096 tokens |
| License | LLaMA 3.1 |
| Model URL | https://huggingface.co/Hibernates/Hiber-Multi-10B-Instruct |
What is Hiber-Multi-10B-Instruct?
Hiber-Multi-10B-Instruct is a state-of-the-art multilingual language model that leverages advanced transformer architecture with 10 billion parameters. Built with a focus on performance and efficiency, it features a 4096 token context length, 32 attention heads, and sophisticated architectural innovations including SwiGLU activation and RMSNorm layer normalization.
Implementation Details
The model employs a decoder-only transformer architecture with 48 layers and a hidden size of 4096. It utilizes an advanced attention mechanism featuring multi-query attention with 32 heads and grouped-query attention with 8 KV heads, optimized through Flash Attention 2.0.
- Rotary Position Embeddings (RoPE) for enhanced positional understanding
- Adaptive KV caching for improved memory efficiency
- Mixture of Experts routing for better task specialization
- 32,000 vocabulary size with optimized tokenization
Core Capabilities
- Efficient processing with varying batch sizes (32-420 tokens/sec on A100)
- Flexible deployment options with INT4/INT8 quantization support
- First token latency of 42ms with scalable throughput
- Optimized memory usage starting from 8GB VRAM (INT4)
Frequently Asked Questions
Q: What makes this model unique?
The model combines advanced architectural features like Flash Attention 2.0, SwiGLU activation, and RMSNorm with efficient multilingual capabilities, making it particularly suitable for production deployments requiring high performance and memory efficiency.
Q: What are the recommended use cases?
The model is well-suited for multilingual applications requiring high throughput, including content generation, translation, and analysis. It performs optimally on systems with NVIDIA Ampere GPUs and 24GB+ VRAM, though it can run on systems with as little as 8GB VRAM using quantization.
