Feature Extraction
Transformers
Safetensors
lfm2
fill-mask
encoder-only
multimodal
image-text-retrieval
image-text-matching
siglip2
lfm2.5
gptq
custom_code
compressed-tensors
Instructions to use konic-labs/LFM2.5-multimodal-encoder-230M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use konic-labs/LFM2.5-multimodal-encoder-230M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="konic-labs/LFM2.5-multimodal-encoder-230M", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("konic-labs/LFM2.5-multimodal-encoder-230M", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("konic-labs/LFM2.5-multimodal-encoder-230M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "Lfm2BidirectionalForMaskedLM" | |
| ], | |
| "auto_map": { | |
| "AutoModel": "modeling_lfm2_bidirectional.Lfm2BidirectionalModel", | |
| "AutoModelForMaskedLM": "modeling_lfm2_bidirectional.Lfm2BidirectionalForMaskedLM" | |
| }, | |
| "block_auto_adjust_ff_dim": false, | |
| "block_dim": 1024, | |
| "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, | |
| "full_attn_idxs": null, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2560, | |
| "layer_types": [ | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "full_attention", | |
| "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": 14, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 0, | |
| "quantization_config": { | |
| "config_groups": { | |
| "group_0": { | |
| "format": "pack-quantized", | |
| "input_activations": null, | |
| "output_activations": null, | |
| "targets": [ | |
| "Linear" | |
| ], | |
| "weights": { | |
| "actorder": "static", | |
| "block_structure": null, | |
| "dynamic": false, | |
| "group_size": 128, | |
| "num_bits": 4, | |
| "observer": "memoryless_minmax", | |
| "observer_kwargs": {}, | |
| "scale_dtype": null, | |
| "strategy": "group", | |
| "symmetric": true, | |
| "type": "int", | |
| "zp_dtype": null | |
| } | |
| } | |
| }, | |
| "format": "pack-quantized", | |
| "global_compression_ratio": null, | |
| "ignore": [ | |
| "lm_head" | |
| ], | |
| "kv_cache_scheme": null, | |
| "quant_method": "compressed-tensors", | |
| "quantization_status": "compressed", | |
| "sparsity_config": {}, | |
| "transform_config": {}, | |
| "version": "0.17.2.a20260729" | |
| }, | |
| "rope_parameters": { | |
| "rope_theta": 1000000.0, | |
| "rope_type": "default" | |
| }, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.14.1", | |
| "use_cache": false, | |
| "use_pos_enc": true, | |
| "vocab_size": 65536 | |
| } |