Stable-Vicuna-13B-HF
| Property | Value |
|---|---|
| Parameter Count | 13 Billion |
| Model Type | Causal Language Model |
| Architecture | LLaMA-based |
| License | CC-BY-NC-SA-4.0 |
| Paper | Research Paper |
What is stable-vicuna-13B-HF?
Stable-Vicuna-13B-HF is an advanced language model built on the LLaMA architecture and fine-tuned using Reinforcement Learning from Human Feedback (RLHF). This model represents a significant evolution in conversational AI, combining the capabilities of Vicuna-13B with enhanced stability and performance through careful optimization.
Implementation Details
The model features a robust architecture with 40 layers and 40 attention heads, utilizing a model dimension of 5120. It's trained using Proximal Policy Optimization (PPO) on a diverse dataset including OpenAssistant Conversations, GPT4All prompt generations, and Alpaca instruction data.
- Implemented in PyTorch with HuggingFace Transformers
- Requires specific prompt template: "### Human: [prompt] ### Assistant:"
- Available in multiple formats including 4-bit GPTQ and GGML variants
Core Capabilities
- High-quality conversational responses and instruction following
- Multi-turn dialogue handling
- Comprehensive knowledge base from diverse training sources
- Optimized for both short responses and extended discussions
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its RLHF training approach and careful optimization using multiple high-quality datasets, making it particularly effective for conversational tasks while maintaining output stability.
Q: What are the recommended use cases?
The model excels in conversational AI applications, instruction following, and general text generation tasks. It's particularly well-suited for applications requiring natural dialogue interaction and complex instruction following.
