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
File size: 1,215 Bytes
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"architectures": [
"Lfm2Model"
],
"block_auto_adjust_ff_dim": true,
"block_dim": 1024,
"block_ff_dim": 6656,
"block_ffn_dim_multiplier": 1.0,
"block_mlp_init_scale": 1.0,
"block_multiple_of": 256,
"block_norm_eps": 1e-05,
"block_out_init_scale": 1.0,
"block_use_swiglu": true,
"block_use_xavier_init": true,
"bos_token_id": 1,
"conv_L_cache": 3,
"conv_bias": false,
"conv_dim": 1024,
"conv_use_xavier_init": true,
"dtype": "float32",
"eos_token_id": 7,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 6656,
"layer_types": [
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv"
],
"max_position_embeddings": 128000,
"model_type": "lfm2",
"norm_eps": 1e-05,
"num_attention_heads": 16,
"num_heads": 16,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 0,
"rope_theta": 1000000.0,
"tie_embedding": true,
"transformers_version": "4.56.2",
"use_cache": true,
"use_pos_enc": true,
"vocab_size": 64402
}
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