Initial

.gitignore

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+.DS_Store

README.md

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+# magic-the-gathering
+
+A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including _Strixhaven_ and _Commander 2021_.
+
+The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card.
+
+Examples of encoded cards:
+
+```
+<|toughness|><|text|>Counter target spell unless its controller pays {X}.<|power|><|type|>Instant<|loyalty|><|manaCost|>{X}{U}<|name|>Clash of Wills
+```
+
+```
+<|loyalty|><|text|>~ enters the battlefield tapped.
+{T}: Add {C}.
+{T}: Add {U} or {R}. ~ deals 1 damage to you.<|toughness|><|name|>Caldera Lake<|power|><|manaCost|><|type|>Land
+```
+
+```
+<|loyalty|>5<|text|>+1: Scry 1, then draw a card.
+−2: Return target creature to its owner's hand.
+−8: You get an emblem with "Whenever an opponent casts their first spell each turn, counter that spell."<|name|>Jace, Unraveler of Secrets<|toughness|><|type|>Legendary Planeswalker — Jace<|manaCost|>{3}{U}{U}<|power|>
+```
+
+The generated cards follow a similar schema, however because the model learns all possible permutations of the schema, the user can prompt the generation with any combination of schema.

config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": ["GPT2LMHeadModel"],
+  "attn_pdrop": 0.2,
+  "bos_token_id": 0,
+  "embd_pdrop": 0.2,
+  "eos_token_id": 0,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-5,
+  "line_by_line": true,
+  "model_type": "gpt2",
+  "n_ctx": 256,
+  "n_embd": 128,
+  "n_head": 8,
+  "n_inner": null,
+  "n_layer": 8,
+  "n_positions": 256,
+  "resid_pdrop": 0.2,
+  "schema_return": null,
+  "schema_tokens": [
+    "<|name|>",
+    "<|manaCost|>",
+    "<|type|>",
+    "<|text|>",
+    "<|power|>",
+    "<|toughness|>",
+    "<|loyalty|>"
+  ],
+  "summary_activation": null,
+  "summary_first_dropout": 0.2,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "transformers_version": "4.5.1",
+  "use_cache": true,
+  "vocab_size": 500
+}

pytorch_model.bin

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tokenizer.json

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