Fix AutoModel loading, add image processor and Apache-2.0 license

Hi, and thanks for releasing these models.

This follows up on the [report on the SigLIP2-Base repo](https://huggingface.co/AvitoTech/SigLIP2-Base-for-animal-identification/discussions).

- **`AutoModel.from_pretrained` did not work.** Every tensor was stored under a `clip.` prefix left over from the training wrapper, which does not match `SiglipModel.base_model_prefix`, so all 1048 parameters were randomly initialised and only a warning was emitted. The tensors are now stored under their canonical names.
- **`config.json`.** `text_config.vocab_size`: `256000`; `text_config.intermediate_size`: `4304`; `text_config.num_hidden_layers`: `27`; `text_config.projection_size`: `1536`; `text_config.hidden_act`: `gelu_pytorch_tanh`; `vision_config.hidden_size`: `1536`; `vision_config.intermediate_size`: `6144`; `vision_config.num_hidden_layers`: `40`; `vision_config.hidden_act`: `gelu_pytorch_tanh`; `vision_dim`: `1536`.
- **Image processor.** Added `preprocessor_config.json`, copied unchanged from `google/siglip2-giant-opt-patch16-384`, so the repo is self-contained and the card no longer has to send users to another repository for preprocessing.
- **License.** Added `license: apache-2.0` and a `LICENSE` file (see below).

### Verification

The reference is the wrapper this checkpoint was trained with: it is built from
`google/siglip2-giant-opt-patch16-384` and loaded from the **published** `model.safetensors` with
`load_state_dict(strict=True)` (0 missing / 0 unexpected keys). Its embeddings are then
compared against `AutoModel.from_pretrained` on the files in this PR.

| check | result |
|---|---|
| missing / unexpected / mismatched keys | 0 / 0 / 0 |
| max abs difference vs. that reference | `0.0` |
| embedding dimensionality | 1536 |
| image processor output vs. `google/siglip2-giant-opt-patch16-384` | identical (max abs diff `0.0`) |

No weight values change anywhere in this PR -- only tensor names, `config.json` and the card.

### One more thing worth flagging

Comparing these weights against the base model, the fine-tune moved only vision blocks 7-11;
the text tower, both projection heads and `logit_scale`/`logit_bias` are byte-identical to
`google/siglip2-giant-opt-patch16-384`. The two towers are therefore no longer aligned, and image-text scoring gives
wrong answers (on a COCO cat photo the model now ranks "a photo of a car" above
"a photo of two cats"). That is expected for a triplet-loss re-identification fine-tune, but
nothing said so, and the Hub still advertises a `zero-shot-image-classification` tag.

For that reason this PR adds only `preprocessor_config.json` and deliberately does **not**
add tokenizer files -- shipping them would make that broken path runnable straight from the
repo. Mentioning it here rather than editing the card, since that is your call to make.

### About the license

The repository currently has no license field and no LICENSE file, which is what prompted
the original question. This PR proposes **Apache-2.0**, matching the other public AvitoTech
models on the Hub and the Apache-2.0 base model(s) this is derived from -- but that call is
yours. If you would rather use different terms, say so and I will amend the PR; if you would
rather add the license yourself, feel free to drop the `LICENSE` file and the frontmatter
line from this PR and take just the loading fix.

LICENSE

@@ -0,0 +1,202 @@
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README.md

@@ -1,4 +1,5 @@
 ---
+license: apache-2.0
 library_name: transformers
 tags:
 - siglip
@@ -7,7 +8,7 @@ tags:
 - clip
 - image-embeddings
 - pet-recognition
-model_id: AvitoTech/SigLIP2-giant-for-animal-identification
+model_id: AvitoTech/SigLIP2-giant
 pipeline_tag: image-feature-extraction
 ---
 
@@ -110,38 +111,22 @@ pip install transformers torch pillow
 
 ```python
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
 from PIL import Image
-from transformers import SiglipModel, SiglipProcessor
-from safetensors.torch import load_file
-from huggingface_hub import hf_hub_download
+from transformers import AutoImageProcessor, AutoModel
 
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        ckpt = "google/siglip2-giant-opt-patch16-384"
-        self.clip = SiglipModel.from_pretrained(ckpt)
-        self.processor = SiglipProcessor.from_pretrained(ckpt)
-
-        
-    def forward(self, images):
-        clip_inputs = self.processor(images=images, return_tensors="pt").to(self.clip.device)
-        return self.clip.get_image_features(**clip_inputs)
-
-model = Model()
-
-weights_path = hf_hub_download(repo_id="AvitoTech/SigLIP2-giant", filename="model.safetensors")
-state_dict = load_file(weights_path)
-model.load_state_dict(state_dict)
+repo = "AvitoTech/SigLIP2-giant"
+processor = AutoImageProcessor.from_pretrained(repo)
+model = AutoModel.from_pretrained(repo).eval()
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model = model.to(device).eval()
+model = model.to(device)
 
 image = Image.open("your_image.jpg").convert("RGB")
 
 with torch.no_grad():
-    embedding = model([image])
+    inputs = processor(images=[image], return_tensors="pt").to(device)
+    embedding = model.get_image_features(**inputs)
     embedding = F.normalize(embedding, dim=1)
 
 print(f"Embedding shape: {embedding.shape}")  # torch.Size([1, 1536])

config.json

@@ -9,18 +9,19 @@
     ],
     "attention_dropout": 0.0,
     "dropout": 0.0,
-    "hidden_act": "gelu",
+    "hidden_act": "gelu_pytorch_tanh",
     "hidden_size": 1152,
     "initializer_factor": 1.0,
     "initializer_range": 0.02,
-    "intermediate_size": 4608,
+    "intermediate_size": 4304,
     "layer_norm_eps": 1e-06,
     "max_position_embeddings": 64,
     "model_type": "siglip_text_model",
     "num_attention_heads": 16,
-    "num_hidden_layers": 32,
+    "num_hidden_layers": 27,
     "pad_token_id": 0,
-    "vocab_size": 32000
+    "vocab_size": 256000,
+    "projection_size": 1536
   },
   "vision_config": {
     "architectures": [
@@ -28,18 +29,18 @@
     ],
     "attention_dropout": 0.0,
     "dropout": 0.0,
-    "hidden_act": "gelu",
-    "hidden_size": 1152,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1536,
     "image_size": 384,
     "initializer_factor": 1.0,
     "initializer_range": 0.02,
-    "intermediate_size": 4608,
+    "intermediate_size": 6144,
     "layer_norm_eps": 1e-06,
     "model_type": "siglip_vision_model",
     "num_attention_heads": 16,
     "num_channels": 3,
-    "num_hidden_layers": 32,
+    "num_hidden_layers": 40,
     "patch_size": 16
   },
-  "vision_dim": 1152
+  "vision_dim": 1536
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fa361d6f7561a3346313acf84e972c9256062814fdaa6dd0840caf84b5d3cb18
-size 7487682160
+oid sha256:f89ebe42d5f059c265f68d9bd2edacce3c9819d6b90b49d5723045e59338c90c
+size 7487676712

preprocessor_config.json

@@ -0,0 +1,24 @@
+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}