Use public model name LESSViT-Base in documentation and examples

README.md

@@ -12,7 +12,7 @@ tags:
 arxiv: 2605.18541
 ---
 
-# LESSViT-b2-d8-r1
+# LESSViT-Base
 
 Pretrained **LESSViT** encoder for hyperspectral representation learning, exported
 from the `LESSVIT_b2_d8_r1` checkpoint. The root of this repository contains an
@@ -43,7 +43,7 @@ import torch
 from transformers import AutoModel
 
 encoder = AutoModel.from_pretrained(
-    "GFM-Bench/LESSViT-b2-d8-r1",
+    "GFM-Bench/LESSViT-Base",
     trust_remote_code=True,
 ).eval()
 
@@ -71,7 +71,7 @@ band_patch_features = features[:, 1:, 1:]  # [1, 10, 4, 768]
 `load_encoder.py` contains the same runnable example:
 
 ```bash
-python load_encoder.py --model-id GFM-Bench/LESSViT-b2-d8-r1 --device cpu
+python load_encoder.py --model-id GFM-Bench/LESSViT-Base --device cpu
 ```
 
 To run offline after downloading a complete snapshot, pass its directory as

SHA256SUMS

@@ -1,8 +1,8 @@
-66bd1865a04156e70e66415709e2ff39fe7dcf71f686002499a6da650274c28c  README.md
+a10bcd03f32b2dd16557b340dfe7ed2bab268a51d45488bc240c077de3710e29  README.md
 d6fcf9ed303752ba5ea7792d3fa00e16ac35cd561b6df23e5a19f4d219be6aac  attention_ops.py
 5aee6c840ac260cde0131ba16ac4c637da27cbe63f94aa0cd5d72f6171436e5d  config.json
 0e049d15e1a8229824e4b87f0d3312f55e58c0a2985bd36a69fa5f7790d024de  hyperspectral_patch_embed.py
-780ee1bf2b7119a3cbc3a4f84f5e185c3f0a8c455f9c23deb401f15556333efe  load_encoder.py
+18968c3e096b908457b47fb35c387609c0e55ac31e9123d4c6e3d6e94fd00019  load_encoder.py
 e1e7af883a62f663010397bc46fcae04801ed2465ed830e8bb488a044f05f809  low_rank_attention.py
 935719728265cf0765b32afe634803ab73071025f104c1c708b98142488b8311  model.safetensors
 1b29142386ee2d45deb01325fdf7cc63e91fd1503ca1017ac0d47db9341e3111  modeling_lessvit.py

load_encoder.py

@@ -4,7 +4,7 @@ import torch
 from transformers import AutoModel
 
 
-def load_encoder(model_id="GFM-Bench/LESSViT-b2-d8-r1", device="cpu", revision=None):
+def load_encoder(model_id="GFM-Bench/LESSViT-Base", device="cpu", revision=None):
     model = AutoModel.from_pretrained(
         model_id, trust_remote_code=True, revision=revision,
     )
@@ -13,7 +13,7 @@ def load_encoder(model_id="GFM-Bench/LESSViT-b2-d8-r1", device="cpu", revision=N
 
 def main():
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument('--model-id', default='GFM-Bench/LESSViT-b2-d8-r1',
+    parser.add_argument('--model-id', default='GFM-Bench/LESSViT-Base',
                         help='Hugging Face model ID or local snapshot directory')
     parser.add_argument('--revision', default=None, help='Optional commit SHA')
     parser.add_argument('--device', default='cpu')