Instructions to use rahul7star/LFM2.5-VL-3B-rahul-tool-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahul7star/LFM2.5-VL-3B-rahul-tool-sft with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rahul7star/LFM2.5-VL-3B-rahul-tool-sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download adapter_model.safetensors from rahul7star/LFM2.5-VL-3B-rahul-tool-sft: direct link, hf CLI and curl.
- Browser
- Download file 80.6 MB
-
https://huggingface.co/rahul7star/LFM2.5-VL-3B-rahul-tool-sft/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://rahul7star/LFM2.5-VL-3B-rahul-tool-sft/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/rahul7star/LFM2.5-VL-3B-rahul-tool-sft/resolve/main/adapter_model.safetensors
80.6 MB
- Xet hash:
- 425e1cf6e585802ff8a1151f80e39ec03293f5c598ae6ea176519b442740fd0d
- Size of remote file:
- 80.6 MB
- SHA256:
- 73b2dff80dfffa99e4f563887beacb898e0333233a4a4b224dd94f45cf6fd2e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.