Neo-7B Language Model
| Property | Value |
|---|---|
| Model Size | 7 Billion parameters |
| Author | m-a-p |
| Paper | arXiv:2405.19327 |
| Training Tokens | 3.7T (pre-training) + 720B (decay phase) |
What is neo_7b?
Neo-7B is a fully open-source large language model that represents a significant advancement in transparent AI development. It's part of the MAP-Neo series, offering complete visibility into its training process, including code, model weights, and datasets. The model comes in multiple variants including base, supervised fine-tuning (SFT), and instruction-tuned versions.
Implementation Details
The model's architecture leverages the transformer-based design and has undergone extensive training across multiple phases. The training process included 3.7T tokens during pre-training and an additional 720B tokens during the decay phase, ensuring robust language understanding and generation capabilities.
- Complete model weights transparency
- Multiple training checkpoints available
- Easy integration with Hugging Face Transformers
- Supports auto-device mapping and various precision formats
Core Capabilities
- Bilingual language understanding and generation
- Text completion and continuation
- Multiple variant support (base, SFT, instruct)
- Scalable implementation from 250M to 7B parameters
Frequently Asked Questions
Q: What makes this model unique?
Neo-7B stands out for its complete transparency in training data, process, and model weights, making it ideal for research and production applications where accountability is crucial.
Q: What are the recommended use cases?
The model is suitable for various applications including text generation, language understanding, and specialized tasks through its different variants (base, SFT, and instruct versions). It's particularly valuable for scenarios requiring transparent AI deployment.
