OPT-30B
| Property | Value |
|---|---|
| Developer | Meta AI (Facebook) |
| Paper | Open Pre-trained Transformer Language Models |
| License | Other (Research-restricted) |
| Training Data | 180B tokens (800GB) |
| Framework | PyTorch |
What is opt-30b?
OPT-30B is part of Meta AI's Open Pretrained Transformer (OPT) series, designed to democratize access to large language models. This 30-billion parameter model represents a significant milestone in open-source AI, trained to match GPT-3's capabilities while promoting transparent research and responsible AI development.
Implementation Details
The model utilizes a decoder-only transformer architecture trained using causal language modeling on a diverse dataset including BookCorpus, CC-Stories, The Pile, Reddit, and CCNewsV2. It employs GPT2's byte-level BPE tokenization with a 50,272 token vocabulary and processes sequences of 2048 tokens.
- Trained on 992 80GB A100 GPUs
- Implements half-precision (float16) for efficient inference
- Supports both deterministic and top-k sampling generation
Core Capabilities
- Text generation and completion
- Zero-shot and few-shot learning
- Research experimentation and analysis
- Fine-tuning for downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
OPT-30B stands out for its open-source nature and research accessibility, allowing researchers to study large language model behavior and address challenges in robustness, bias, and toxicity. It's one of the few publicly available models of this scale.
Q: What are the recommended use cases?
The model is best suited for research purposes, text generation tasks, and fine-tuning for specific applications. However, users should be aware of potential biases and limitations, particularly in generating content that may reflect societal biases present in the training data.
