State-of-the-art transformer model fine-tuned on millions of patent documents for specialized intellectual property tasks including generation, analysis, and semantic search.
Generate claims, specifications, abstracts
Semantic similarity matching
Entity extraction, summarization
Claim comparison and mapping
• Architecture: Decoder-only transformer with rotary position embeddings (RoPE)
• Attention: Multi-query attention for efficient inference
• Tokenizer: Custom BPE with 64K vocabulary including patent-specific terms
• Training: Mixed-precision (BF16) with gradient checkpointing
• Inference: Quantization support (INT8, INT4) for deployment
Patent Text
32K tokens
Custom BPE
64K vocab
4096 dim
+ RoPE
32 Layers
32 Heads
Linear
64K output
Generated
Text
| Component | Specification | Details |
|---|---|---|
| Hidden Dimension | 4096 | Per-layer representation size |
| Intermediate Dimension | 11008 | FFN hidden layer (SwiGLU) |
| Attention Heads | 32 | Multi-head self-attention |
| KV Heads | 8 | Grouped-query attention |
| Layers | 32 | Transformer decoder blocks |
| Vocabulary Size | 64,000 | Custom patent tokenizer |
| Context Length | 32,768 | Maximum sequence length |
• 100M+ USPTO patents (1976-2023)
• 50M+ EPO, WIPO, and CNIPA patents
• 10M+ legal documents and case law
• 5M+ scientific papers
• Curated instruction dataset (500K examples)
• AdamW optimizer (β1=0.9, β2=0.95)
• Cosine learning rate schedule
• Linear warmup (2000 steps)
• Gradient clipping (1.0)
• Mixed precision (BF16)
| Task | LUMA-7B | GPT-4 | Claude-3 | Llama-3-70B |
|---|---|---|---|---|
| Patent Claim Generation | 94.2% | 89.1% | 87.5% | 82.3% |
| Prior Art Retrieval (R@10) | 92.8% | 85.4% | 84.2% | 78.9% |
| Entity Extraction (F1) | 96.4% | 91.2% | 90.8% | 86.5% |
| Document Summarization | 91.7% | 92.3% | 91.1% | 87.2% |
| IPC Classification | 97.1% | 88.5% | 87.9% | 83.4% |
| Infringement Analysis | 89.3% | 84.7% | 83.2% | 76.8% |