BERTIN GPT-J-6B
| Property | Value |
|---|---|
| Parameter Count | 6.06B |
| Model Type | Causal Language Model |
| Language | Spanish |
| License | Apache 2.0 |
| Training Data | mC4-es-sampled |
| Architecture | GPT-J with RoPE |
What is bertin-gpt-j-6B?
BERTIN GPT-J-6B is a Spanish language model that represents a significant advancement in Spanish natural language processing. Fine-tuned from the original GPT-J architecture, this model contains 6.06 billion parameters and was trained on a carefully curated Spanish subset of mC4 data. The model underwent extensive training for approximately 65 billion tokens over 1 million steps using TPU v3-8 VM hardware.
Implementation Details
The model architecture features 28 layers with a model dimension of 4096 and a feedforward dimension of 16384. It implements advanced features such as Rotary Position Embedding (RoPE) applied to 64 dimensions of each of its 16 attention heads. The model maintains compatibility with GPT-2/3 tokenization, utilizing a vocabulary size of 50257 tokens.
- 28 transformer layers with self-attention and feedforward blocks
- 4096 model dimension with 16 attention heads
- 16384 feedforward dimension
- 2048 context window size
- Rotary Position Embedding for enhanced position awareness
Core Capabilities
- Spanish text generation with high coherence and fluency
- Zero-shot reading comprehension and reasoning in Spanish
- Feature extraction for downstream Spanish NLP tasks
- Context window of 2048 tokens for handling longer texts
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Spanish language processing, trained on a carefully sampled dataset using perplexity-based selection. It combines the powerful GPT-J architecture with Spanish-specific optimizations, making it particularly effective for Spanish language tasks.
Q: What are the recommended use cases?
The model excels at text generation tasks in Spanish, including content creation, completion, and augmentation. However, users should be aware that human curation is recommended for output quality control, and the model should not be relied upon for factually critical applications.
