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chess-submission-v6-fixed

Text Generation
Transformers
PyTorch
chess_lm
chess
llm-course
chess-challenge
custom_code
Model card Files
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 Community
MDaytek commited on
Commit
c5ebb77
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1 Parent(s): 07ab826

v6-fixed

Browse files
Files changed (6) hide show
  1. README.md +12 -0
  2. config.json +18 -0
  3. model.py +69 -0
  4. pytorch_model.bin +3 -0
  5. tokenizer.py +38 -0
  6. vocab.json +1352 -0
README.md ADDED
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1
+ ---
2
+ library_name: transformers
3
+ tags:
4
+ - chess
5
+ - llm-course
6
+ - chess-challenge
7
+ license: mit
8
+ ---
9
+ # v6-fixed
10
+ User: MDaytek
11
+ Params: 998,912
12
+ Vocab: 1350 real tokens
config.json ADDED
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1
+ {
2
+ "architectures": [
3
+ "ChessForCausalLM"
4
+ ],
5
+ "dtype": "float32",
6
+ "model_type": "chess_lm",
7
+ "n_ctx": 256,
8
+ "n_embd": 128,
9
+ "n_head": 4,
10
+ "n_layer": 4,
11
+ "n_positions": 256,
12
+ "transformers_version": "4.57.3",
13
+ "vocab_size": 1350,
14
+ "auto_map": {
15
+ "AutoConfig": "model.ChessConfig",
16
+ "AutoModelForCausalLM": "model.ChessForCausalLM"
17
+ }
18
+ }
model.py ADDED
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1
+
2
+ import torch
3
+ import torch.nn as nn
4
+ from transformers import PreTrainedModel, PretrainedConfig
5
+ from transformers.modeling_outputs import CausalLMOutputWithPast
6
+
7
+ class ChessConfig(PretrainedConfig):
8
+ model_type = "chess_lm"
9
+
10
+ def __init__(self, vocab_size=1000, n_positions=256, n_embd=128, n_layer=4,
11
+ n_head=4, n_ctx=256, tie_word_embeddings=True, **kwargs):
12
+ self.vocab_size = vocab_size
13
+ self.n_positions = n_positions
14
+ self.n_embd = n_embd
15
+ self.n_layer = n_layer
16
+ self.n_head = n_head
17
+ self.n_ctx = n_ctx
18
+ self.tie_word_embeddings = tie_word_embeddings
19
+ super().__init__(**kwargs)
20
+
21
+ class ChessForCausalLM(PreTrainedModel):
22
+ config_class = ChessConfig
23
+
24
+ def __init__(self, config):
25
+ super().__init__(config)
26
+ self.config = config
27
+ self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd)
28
+ self.position_embedding = nn.Embedding(config.n_positions, config.n_embd)
29
+ encoder_layer = nn.TransformerEncoderLayer(
30
+ d_model=config.n_embd,
31
+ nhead=config.n_head,
32
+ dim_feedforward=config.n_embd * 4,
33
+ batch_first=True,
34
+ norm_first=True
35
+ )
36
+ self.blocks = nn.TransformerEncoder(encoder_layer, num_layers=config.n_layer)
37
+ self.ln_f = nn.LayerNorm(config.n_embd)
38
+ self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
39
+ if config.tie_word_embeddings:
40
+ self.head.weight = self.token_embedding.weight
41
+ self.post_init()
42
+
43
+ def get_input_embeddings(self):
44
+ return self.token_embedding
45
+
46
+ def set_input_embeddings(self, value):
47
+ self.token_embedding = value
48
+
49
+ def forward(self, input_ids, labels=None, **kwargs):
50
+ B, T = input_ids.shape
51
+ tok_emb = self.token_embedding(input_ids)
52
+ pos_emb = self.position_embedding(torch.arange(T, device=input_ids.device))
53
+ x = tok_emb + pos_emb
54
+ mask = torch.triu(torch.ones(T, T, device=input_ids.device) * float('-inf'), diagonal=1)
55
+ x = self.blocks(x, mask=mask, is_causal=True)
56
+ x = self.ln_f(x)
57
+ logits = self.head(x)
58
+
59
+ loss = None
60
+ if labels is not None:
61
+ shift_logits = logits[..., :-1, :].contiguous()
62
+ shift_labels = labels[..., 1:].contiguous()
63
+ loss_fct = nn.CrossEntropyLoss()
64
+ loss = loss_fct(
65
+ shift_logits.view(-1, self.config.vocab_size),
66
+ shift_labels.view(-1)
67
+ )
68
+
69
+ return CausalLMOutputWithPast(loss=loss, logits=logits)
pytorch_model.bin ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:f3fa56fb5de53f71d167b6e66264101846d3711ef3032f49c917cc2e5742d9cc
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+ size 4013155
tokenizer.py ADDED
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1
+
2
+ import json, os, torch
3
+ class ChessTokenizer:
4
+ def __init__(self, vocab=None):
5
+ self.vocab = vocab if vocab else {}
6
+ self.id_to_token = {v: k for k, v in self.vocab.items()}
7
+ self.pad_token_id = self.vocab.get("[PAD]", 0)
8
+ self.bos_token_id = self.vocab.get("[BOS]", 1)
9
+ self.eos_token_id = self.vocab.get("[EOS]", 2)
10
+
11
+ @property
12
+ def vocab_size(self):
13
+ return len(self.vocab)
14
+
15
+ def _convert_token_to_id(self, token):
16
+ return self.vocab.get(token, self.vocab.get("[UNK]"))
17
+
18
+ def pad(self, encoded_inputs, padding=True, max_length=None, pad_to_multiple_of=None, return_tensors=None):
19
+ batch_ids = [x["input_ids"] for x in encoded_inputs]
20
+ max_len = max(len(ids) for ids in batch_ids)
21
+ padded_batch = []
22
+ for ids in batch_ids:
23
+ padded_ids = ids + [self.pad_token_id] * (max_len - len(ids))
24
+ padded_batch.append(padded_ids)
25
+ if return_tensors == "pt":
26
+ return {"input_ids": torch.tensor(padded_batch, dtype=torch.long)}
27
+ return {"input_ids": padded_batch}
28
+
29
+ def save_pretrained(self, save_directory):
30
+ os.makedirs(save_directory, exist_ok=True)
31
+ with open(os.path.join(save_directory, "vocab.json"), "w") as f:
32
+ json.dump(self.vocab, f, indent=4)
33
+
34
+ @classmethod
35
+ def from_pretrained(cls, load_directory):
36
+ with open(os.path.join(load_directory, "vocab.json"), "r") as f:
37
+ vocab = json.load(f)
38
+ return cls(vocab)
vocab.json ADDED
@@ -0,0 +1,1352 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "[PAD]": 0,
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+ "[BOS]": 1,
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+ "[EOS]": 2,
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+ "[UNK]": 3,
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