OPT-6.7B
| Property | Value |
|---|---|
| Developer | Meta AI (Facebook) |
| Paper | Open Pre-trained Transformer Language Models |
| License | Other (Research Only) |
| Training Data | 180B tokens (800GB) |
| Architecture | Decoder-only Transformer |
What is opt-6.7b?
OPT-6.7B is part of Meta AI's Open Pre-trained Transformer (OPT) series, designed to democratize access to large language models. This 6.7 billion parameter model represents a significant milestone in open-source AI, trained to match GPT-3's capabilities while promoting transparent and responsible AI research.
Implementation Details
The model implements a decoder-only transformer architecture trained using causal language modeling. It utilizes the GPT2 tokenizer with a 50,272 token vocabulary and processes sequences of 2,048 tokens. The training corpus combines diverse sources including BookCorpus, CC-Stories, The Pile, Reddit data, and CCNewsV2.
- Trained on 180B tokens across multiple datasets
- Uses half-precision (float16) for efficient inference
- Supports both deterministic and sampling-based text generation
- Requires GPU acceleration for optimal performance
Core Capabilities
- Text generation and completion
- Zero-shot and few-shot learning
- Language understanding and processing
- Prompt-based task solving
Frequently Asked Questions
Q: What makes this model unique?
OPT-6.7B stands out for its open-source nature and focus on research accessibility, while matching the capabilities of similar-sized proprietary models. It's designed for responsible AI research and comes with comprehensive documentation about its limitations and biases.
Q: What are the recommended use cases?
The model is best suited for research purposes, text generation tasks, and fine-tuning for specific applications. It's particularly useful for studying model behavior, bias analysis, and developing more robust AI systems.
