Sentence Similarity
Safetensors
sentence-transformers
PyLate
lfm2
liquid
edge
ColBERT
feature-extraction
Eval Results (legacy)
Instructions to use LiquidAI/LFM2-ColBERT-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LiquidAI/LFM2-ColBERT-350M with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="LiquidAI/LFM2-ColBERT-350M") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -816,7 +816,6 @@ We recommend using this model for various RAG use cases, such as:
|
|
| 816 |
| --------------------- | ------------------------------ |
|
| 817 |
| **Total parameters** | 353,322,752 |
|
| 818 |
| **Layers** | 17 (10 conv + 6 attn + 1 dense)|
|
| 819 |
-
| **Context length** | 32,768 tokens |
|
| 820 |
| **Vocabulary size** | 64,402 |
|
| 821 |
| **Training precision**| BF16 |
|
| 822 |
| **License** | LFM Open License v1.0 |
|
|
|
|
| 816 |
| --------------------- | ------------------------------ |
|
| 817 |
| **Total parameters** | 353,322,752 |
|
| 818 |
| **Layers** | 17 (10 conv + 6 attn + 1 dense)|
|
|
|
|
| 819 |
| **Vocabulary size** | 64,402 |
|
| 820 |
| **Training precision**| BF16 |
|
| 821 |
| **License** | LFM Open License v1.0 |
|