Starling-LM-7B-alpha
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| Base Model | Openchat 3.5 (Mistral-7B-v0.1) |
| License | Apache-2.0 |
| Training Method | RLAIF with APA |
| MT Bench Score | 8.09 |
What is Starling-LM-7B-alpha?
Starling-LM-7B-alpha is a state-of-the-art language model developed by Berkeley NEST, representing a significant advancement in open-source AI. Built upon Openchat 3.5, it utilizes Reinforcement Learning from AI Feedback (RLAIF) and achieves remarkable performance metrics that position it just below GPT-4 and GPT-4 Turbo in benchmarks.
Implementation Details
The model employs a sophisticated training approach combining C-RLFT with Advantage-induced Policy Alignment (APA). It leverages the berkeley-nest/Nectar dataset for training and implements specific chat templates for optimal performance.
- Built on Mistral-7B architecture with 7.24B parameters
- Utilizes BF16 tensor type for efficient computation
- Implements specific conversation templates for different use cases
- Supports both single-turn and multi-turn conversations
Core Capabilities
- Achieves 8.09 on MT Bench evaluation
- 91.99 score on AlpacaEval
- 63.9 score on MMLU
- Specialized support for coding tasks
- Enhanced conversational abilities
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its exceptional performance achieved through RLAIF training, making it one of the most capable open-source 7B parameter models available. It outperforms many larger models while maintaining efficient resource usage.
Q: What are the recommended use cases?
The model excels in conversational AI applications, coding assistance, and general text generation tasks. It's particularly well-suited for applications requiring high-quality responses while maintaining reasonable computational requirements.
