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
| { | |
| "pairs": "/data/datasets/monet-100k/train_pairs.jsonl", | |
| "val_pairs": "/data/datasets/monet-100k/validation_pairs.jsonl", | |
| "model": "/data/models/LFM-2.5-Encoder-230M", | |
| "vision_model": "google/siglip2-base-patch16-256", | |
| "output": "outputs/monet-100k/projector-siglip2-clean", | |
| "num_image_tokens": 32, | |
| "batch_size": 8, | |
| "steps": 1000, | |
| "max_text_length": 128, | |
| "lr": 0.0001, | |
| "temperature": 0.07, | |
| "dtype": "float32", | |
| "seed": 42, | |
| "eval_every": 250, | |
| "eval_pairs": 256, | |
| "image_only": true, | |
| "device": "cuda:0", | |
| "trust_remote_code": false, | |
| "force": true | |
| } | |