BigScience TR11-176B Language Model
| Property | Value |
|---|---|
| Parameter Count | 176 Billion |
| Architecture Type | Decoder-only (GPT-like) |
| Training Infrastructure | 384 A100 80GB GPUs |
| Languages Supported | 46 languages |
| Dataset Size | 341.6B tokens (1.5TB) |
What is tr11-176B-logs?
The tr11-176B-logs represents one of the most ambitious open-source language model training projects, developed by BigScience with collaboration from over 1000 researchers worldwide. This massive multilingual model features 176 billion parameters and was trained on the Jean Zay supercomputer using environmentally conscious infrastructure.
Implementation Details
The model architecture consists of 70 layers with 112 attention heads per layer and a hidden dimensionality of 14336. It utilizes ALiBi positional embeddings and GeLU activation functions, with a sequence length of 2048 tokens.
- Checkpoint size: 329GB (bf16 weights), 2.3TB (full with optimizer states)
- Training throughput: ~150 TFLOPs
- Training duration: 3-4 months
- Tokenizer vocabulary: 250,680 tokens
Core Capabilities
- Multilingual processing across 46 languages including low-resource languages
- Large-scale text generation and understanding
- Efficient distributed training capabilities
- Environmental consciousness with low-carbon nuclear power usage
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its open collaborative development, massive scale, and focus on multilingual capabilities while maintaining environmental responsibility. It's one of the largest openly trained language models with comprehensive documentation of its training process.
Q: What are the recommended use cases?
The model is designed for multilingual natural language processing tasks, particularly beneficial for languages with limited resources. It's suitable for research and applications requiring deep language understanding across multiple languages.
