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
| { | |
| "calibration_kind": "fused_inputs_embeds", | |
| "calibration_samples": 256, | |
| "calibration_source": "outputs/monet-100k/calibration-fused-clean/fused_calibration.pt", | |
| "compressor_commit": "8cec0acc1931de6f8f73257151ab7007c14dbf4e", | |
| "compressor_repo": "/data/llm-compressor", | |
| "device": "cuda:0", | |
| "dtype": "torch.float32", | |
| "max_seq_length": 160, | |
| "method": "gptq-int4", | |
| "notes": "Re-run scripts/evaluate.py before trusting task quality.", | |
| "source_model": "outputs/monet-100k/reference-bf16-clean" | |
| } | |