Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- yamboo/Griffin_datasets_joint_v65
+- yamboo/Griffin_datasets_single_pretrain_v3
+metrics:
+- accuracy
+- roc_auc
+- mse
+pipeline_tag: graph-ml
+---
+
+# Griffin: Pretrained Checkpoints
+
+This repository contains various pretrained checkpoints for the [Griffin model](https://github.com/yanxwb/Griffin).
+
+## Checkpoints
+
+The checkpoints are organized as follows:
+
+```bash
+./checkpoints/
+├── single-completion # Pretrained single table completion model.
+├── single-sft # Pretrained single table SFT model. Used in main experiments.
+└── transfer # Pretrained transfer model. Used in transfer experiments.
+    ├── commerce-1 # Split name.
+       ├── FULL # RDB-SFT setting name. This one used in main transfer experiments.
+       ├── MIXED # RDB-SFT setting name. Used in ablation in RDB-SFT setting.
+       └── LIMITED # RDB-SFT setting name. Used in ablation in RDB-SFT setting.
+    ├── commerce-2 # Same as above.
+       ├── FULL
+       ├── MIXED
+       └── LIMITED
+    ├── others-1
+       ├── FULL
+       ├── MIXED
+       └── LIMITED
+    └── others-2
+       ├── FULL
+       ├── MIXED
+       └── LIMITED
+```
+
+## How to use
+
+To get started, you will need to have the model's architecture defined in your code, provided in [Github Repo](https://github.com/yanxwb/Griffin). You can then use the `huggingface_hub` library to download a specific checkpoint and load its weights.
+
+```python
+import json
+import torch
+from huggingface_hub import hf_hub_download
+import accelerate
+
+# Assume 'GriffinModel' is your model's class definition
+# from your_project_position.hmodel import GriffinMod
+
+# 1. Define the repository ID and the specific file you want to load
+repo_id = "yamboo/Griffin_models"
+# Example: Loading the main single-table SFT model
+checkpoint_path = "single-sft/model.safetensors"
+config_path = "single-sft/config.json"
+
+
+# 2. Download the checkpoint file from the Hub
+model_weights_path = hf_hub_download(repo_id=repo_id, filename=checkpoint_path)
+model_config_path = hf_hub_download(repo_id=repo_id, filename=config_path)
+config = json.load(open("config.json", "r"))
+
+# 3. Instantiate your model and load the weights. We use accelerate to align with Github repo experiment pipeline.
+model = GriffinMod(**config)  # Make sure to pass any required config
+accelerate.load_checkpoint_in_model(model, model_weights_path)
+
+```