Release Table 2 vision expert

LICENSE

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+MIT License
+
+Copyright (c) 2021 OpenAI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

README.md

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+---
+license: mit
+base_model: openai/clip-vit-base-patch32
+tags:
+- smat
+- model-merging
+- vision
+- arxiv:2609.33437
+---
+# CLIP ViT base-patch32 · SMAT · DTD
+
+A SMAT expert from Table 2 of **SMAT: Simple and Efficient Merge-Aware Training** (seed 42). SMAT trains experts with model merging in mind.
+
+[Paper](https://arxiv.org/abs/2609.33437) · [GitHub & usage](https://github.com/egangu/smat/blob/main/docs/HUGGINGFACE.md)
+
+`encoder.pt` is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.

encoder.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:144dfca93eb0dfb08b5ea437e5f6d18cf6b768a5b63029ac74ede37fc6a9d95d
+size 349896625

metadata.json

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+{
+  "experiment": "clip8_vit",
+  "task": "DTD",
+  "seed": 42,
+  "steps": 4000,
+  "epochs": null,
+  "train": {
+    "optimizer": "adam",
+    "method": "smat",
+    "batch_size": 128,
+    "learning_rate": 1e-05,
+    "weight_decay": 0.0,
+    "max_steps": 4000,
+    "schedule": "cosine",
+    "warmup_steps": 0,
+    "num_workers": 8,
+    "fused": true,
+    "regularizer": {
+      "name": "none",
+      "lambda": 0.1
+    },
+    "smat": {
+      "mode": "joint",
+      "interval": 4
+    },
+    "backend": "fast",
+    "scale": {
+      "alpha_min": 0.1,
+      "scope": "global"
+    },
+    "mask": {
+      "probability": 0.5,
+      "scope": "block-linear",
+      "embedding_probability": 0
+    },
+    "perturb": {
+      "rms": 0.001
+    },
+    "log_every": 20
+  }
+}