Upload T5ForConditionalGeneration

README.md

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+---
+tags:
+- adapter-transformers
+- t5
+datasets:
+- length_ctrl
+---
+
+# Adapter `dyxohjl666/xlora` for google/flan-t5-xl
+
+An [adapter](https://adapterhub.ml) for the `google/flan-t5-xl` model that was trained on the [length_ctrl](https://huggingface.co/datasets/length_ctrl/) dataset.
+
+This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
+
+## Usage
+
+First, install `adapters`:
+
+```
+pip install -U adapters
+```
+
+Now, the adapter can be loaded and activated like this:
+
+```python
+from adapters import AutoAdapterModel
+
+model = AutoAdapterModel.from_pretrained("google/flan-t5-xl")
+adapter_name = model.load_adapter("dyxohjl666/xlora", source="hf", set_active=True)
+```
+
+## Architecture & Training
+
+<!-- Add some description here -->
+
+## Evaluation results
+
+<!-- Add some description here -->
+
+## Citation
+
+<!-- Add some description here -->

adapter_config.json

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+{
+  "config": {
+    "alpha": 8,
+    "architecture": "lora",
+    "attn_matrices": [
+      "q",
+      "v"
+    ],
+    "composition_mode": "add",
+    "dropout": 0.0,
+    "init_weights": "lora",
+    "intermediate_lora": false,
+    "leave_out": [],
+    "output_lora": false,
+    "r": 8,
+    "selfattn_lora": true,
+    "use_gating": false
+  },
+  "hidden_size": 2048,
+  "model_class": "T5ForConditionalGeneration",
+  "model_name": "google/flan-t5-xl",
+  "model_type": "t5",
+  "name": "length",
+  "version": "0.2.2"
+}

head_config.json

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+{
+  "config": null,
+  "hidden_size": 2048,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "model_class": "T5ForConditionalGeneration",
+  "model_name": "google/flan-t5-xl",
+  "model_type": "t5",
+  "name": null,
+  "num_labels": 2,
+  "version": "0.2.2"
+}

pytorch_adapter.bin

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+size 18974226

pytorch_model_head.bin

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