Upload 4 files

__init__.py

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+from .configuration_gpt import CustomGPTConfig
+from .modeling_gpt import CustomGPT
+
+CustomGPTConfig.register_for_auto_class()
+CustomGPT.register_for_auto_class("AutoModelForCausalLM")

config.json

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+{
+  "architectures": [
+    "CustomGPT"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_gpt.CustomGPTConfig",
+    "AutoModelForCausalLM": "modeling_gpt.CustomGPT"
+  },
+  "block_size": 768,
+  "dropout": 0.1,
+  "model_type": "custom_gpt",
+  "n_embd": 768,
+  "n_head": 8,
+  "n_layer": 8,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "vocab_size": 50257
+}

configuration_gpt.py

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+from dataclasses import dataclass
+from transformers import PretrainedConfig
+
+@dataclass
+class CustomGPTConfig(PretrainedConfig):
+    """
+    Configuration class for custom GPT model.
+    """
+    model_type = "custom_gpt"
+    block_size: int = 768
+    vocab_size: int = 50257
+    n_layer: int = 8
+    n_head: int = 8
+    n_embd: int = 768
+    dropout: float = 0.1

modeling_gpt.py

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+import os
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from transformers import PreTrainedModel, AutoConfig, AutoModelForCausalLM
+from .configuration_gpt import CustomGPTConfig  # Use relative import
+from huggingface_hub import HfApi
+from huggingface_hub import HfApi, create_repo
+
+# Define the CausalSelfAttention class
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+
+    def forward(self, x):
+        B, T, C = x.size()
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.c_proj(y)
+        return y
+
+# Define the MLP class
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
+        self.gelu = nn.GELU(approximate='tanh')
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        return x
+
+# Define the Block class
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+# Define the GPT class
+class CustomGPT(PreTrainedModel):
+    config_class = CustomGPTConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.transformer = nn.ModuleDict(dict(
+            wte=nn.Embedding(config.vocab_size, config.n_embd),
+            wpe=nn.Embedding(config.block_size, config.n_embd),
+            h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f=nn.LayerNorm(config.n_embd),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.transformer.wte.weight = self.lm_head.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.size()
+        assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
+        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
+        pos_emb = self.transformer.wpe(pos)
+        tok_emb = self.transformer.wte(idx)
+        x = tok_emb + pos_emb
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        logits = self.lm_head(x)
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+        return logits, loss
+
+    def save_pretrained(self, save_directory, safe_serialization=False):
+        # Ensure the save directory exists
+        if not os.path.exists(save_directory):
+            os.makedirs(save_directory)
+            print(f"Created directory: {save_directory}")
+        else:
+            print(f"Directory already exists: {save_directory}")
+
+        # Save the model configuration
+        config_path = os.path.join(save_directory, "config.json")
+        self.config.save_pretrained(save_directory)
+        print(f"Saved configuration to: {config_path}")
+
+        # Save the model weights
+        model_path = os.path.join(save_directory, "pytorch_model.bin")
+        torch.save(self.state_dict(), model_path)
+        print(f"Saved model weights to: {model_path}")
+
+        # If safe_serialization is False, call the base class method
+        if not safe_serialization:
+            super().save_pretrained(save_directory, safe_serialization=False)
+        
+
+        
+
+
+
+    def push_to_hub(self, repo_id, commit_message="Push model to hub"):
+        try:
+            # Save the model locally
+            self.save_pretrained(repo_id)
+            print(f"Model saved locally to {repo_id}")
+
+            # Create the repository with the desired privacy settings
+            api = HfApi()
+            api.create_repo(repo_id=repo_id, private=False, exist_ok=True)
+            print(f"Repository created (or already exists) with ID: {repo_id}")
+
+            # Use HfApi to push the model to the Hugging Face Hub
+            api.upload_folder(
+                folder_path=repo_id,
+                repo_id=repo_id,
+                repo_type="model",
+                commit_message=commit_message
+            )
+            print(f"Model uploaded successfully to {repo_id}")
+        except Exception as e:
+            print(f"Failed to upload model: {e}")
+
+