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README.md

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+# bayes_mini
+
+`bayes_mini` is a custom GPT-2 (124M) language model trained from scratch on ~20 GB of English Wikipedia data.
+
+## Architecture
+
+- Based on GPT-2 small (124M parameters)
+- 12 layers, 12 attention heads
+- Hidden size: 768
+- Context length: 1024
+- Vocabulary size: 50257
+- Dropout: 0.1
+
+## Training Configuration
+
+- Dataset: Cleaned English Wikipedia (~20 GB)
+- Architecture: GPT-2 Small (124M parameters)
+- Optimizer settings: `Foundation better_quality`
+- Hardware: NVIDIA GeForce RTX 4060 (8 GB VRAM)
+- Epochs: 50
+- Batch size: 4 (gradient accumulation steps: 8 -> effective batch size: 32)
+- Learning rate: 2e-4
+- Warmup steps: 2000
+- Weight decay: 0.01
+
+
+## Install required packages
+```bash
+pip install torch transformers tiktoken huggingface_hub
+```
+
+## Example Usage
+
+```python
+import os
+import torch
+import json
+import tiktoken
+import importlib.util
+from huggingface_hub import hf_hub_download
+
+# === CONFIG ===
+REPO_ID = "faizack/bayes_mini_custom"
+
+# === Step 1: Download necessary files ===
+config_path = hf_hub_download(repo_id=REPO_ID, filename="config.json")
+model_path = hf_hub_download(repo_id=REPO_ID, filename="pytorch_model.bin")
+modeling_path = hf_hub_download(repo_id=REPO_ID, filename="modeling_gpt2_custom.py")
+
+# === Step 2: Dynamically import modeling_gpt2_custom.py ===
+spec = importlib.util.spec_from_file_location("modeling_gpt2_custom", modeling_path)
+mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(mod)
+GPTModel = mod.GPTModel  # Now you can use GPTModel
+
+# === Step 3: Load config ===
+with open(config_path, "r") as f:
+    config = json.load(f)
+
+model_config = {
+    "vocab_size": config["vocab_size"],
+    "context_length": config["n_positions"],
+    "emb_dim": config["n_embd"],
+    "n_heads": config["n_head"],
+    "n_layers": config["n_layer"],
+    "drop_rate": config["dropout"],
+    "qkv_bias": config["qkv_bias"],
+}
+
+# === Step 4: Load tokenizer ===
+tokenizer = tiktoken.get_encoding("gpt2")
+prompt = "The rise of artificial intelligence"
+input_ids = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
+
+# === Step 5: Load model ===
+model = GPTModel(model_config)
+model.load_state_dict(torch.load(model_path, map_location="cpu"))
+model.eval()
+
+
+# === Step 6: Generate ===
+def generate(model, idx, max_new_tokens=50):
+    for _ in range(max_new_tokens):
+        idx_cond = idx[:, -model_config["context_length"] :]
+        with torch.no_grad():
+            logits = model(idx_cond)
+        logits = logits[:, -1, :]
+        probs = torch.softmax(logits, dim=-1)
+        next_token = torch.multinomial(probs, num_samples=1)
+        idx = torch.cat([idx, next_token], dim=1)
+    return idx
+
+
+output = generate(model, input_ids)
+print(tokenizer.decode(output[0].tolist()))
+
+```

config.json

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+{
+    "architectures": [
+        "GPTModel"
+    ],
+    "model_type": "gpt2",
+    "vocab_size": 50257,
+    "n_positions": 1024,
+    "n_embd": 768,
+    "n_layer": 12,
+    "n_head": 12,
+    "dropout": 0.1,
+    "qkv_bias": true
+}

modeling_gpt2_custom.py

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+import tiktoken
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+
+
+class GPTDatasetV1(Dataset):
+    def __init__(self, txt, tokenizer, max_length, stride):
+        self.input_ids = []
+        self.target_ids = []
+
+        # Check if txt is a generator (streaming mode)
+        if hasattr(txt, "__iter__") and not isinstance(txt, (str, bytes)):
+            # Process data in chunks for streamed input
+            all_tokens = []
+            for chunk in txt:
+                if isinstance(chunk, str):
+                    chunk_tokens = tokenizer.encode(
+                        chunk, allowed_special={"<|endoftext|>"}
+                    )
+                    all_tokens.extend(chunk_tokens)
+
+                    # Process accumulated tokens when we have enough for at least one sequence
+                    while len(all_tokens) >= max_length + 1:
+                        input_chunk = all_tokens[:max_length]
+                        target_chunk = all_tokens[1 : max_length + 1]
+                        self.input_ids.append(torch.tensor(input_chunk))
+                        self.target_ids.append(torch.tensor(target_chunk))
+
+                        # Remove processed tokens with stride
+                        all_tokens = all_tokens[stride:]
+        else:
+            # Original implementation for string input
+            token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
+
+            # Use a sliding window to chunk the book into overlapping sequences of max_length
+            for i in range(0, len(token_ids) - max_length, stride):
+                input_chunk = token_ids[i : i + max_length]
+                target_chunk = token_ids[i + 1 : i + max_length + 1]
+                self.input_ids.append(torch.tensor(input_chunk))
+                self.target_ids.append(torch.tensor(target_chunk))
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, idx):
+        return self.input_ids[idx], self.target_ids[idx]
+
+
+def create_dataloader_v1(
+    txt,
+    batch_size=4,
+    max_length=256,
+    stride=128,
+    shuffle=True,
+    drop_last=True,
+    num_workers=0,
+):
+    # Initialize the tokenizer
+    tokenizer = tiktoken.get_encoding("gpt2")
+
+    # Create dataset
+    dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
+
+    # Create dataloader
+    dataloader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        drop_last=drop_last,
+        num_workers=num_workers,
+    )
+
+    return dataloader
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
+        super().__init__()
+        assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
+
+        self.d_out = d_out
+        self.num_heads = num_heads
+        self.head_dim = (
+            d_out // num_heads
+        )  # Reduce the projection dim to match desired output dim
+
+        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.out_proj = nn.Linear(d_out, d_out)  # Linear layer to combine head outputs
+        self.dropout = nn.Dropout(dropout)
+        self.register_buffer(
+            "mask", torch.triu(torch.ones(context_length, context_length), diagonal=1)
+        )
+
+    def forward(self, x):
+        b, num_tokens, d_in = x.shape
+
+        keys = self.W_key(x)  # Shape: (b, num_tokens, d_out)
+        queries = self.W_query(x)
+        values = self.W_value(x)
+
+        # We implicitly split the matrix by adding a `num_heads` dimension
+        # Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
+        keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
+        values = values.view(b, num_tokens, self.num_heads, self.head_dim)
+        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
+
+        # Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
+        keys = keys.transpose(1, 2)
+        queries = queries.transpose(1, 2)
+        values = values.transpose(1, 2)
+
+        # Compute scaled dot-product attention (aka self-attention) with a causal mask
+        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head
+
+        # Original mask truncated to the number of tokens and converted to boolean
+        mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
+
+        # Use the mask to fill attention scores
+        attn_scores.masked_fill_(mask_bool, -torch.inf)
+
+        attn_weights = torch.softmax(attn_scores / keys.shape[-1] ** 0.5, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+
+        # Shape: (b, num_tokens, num_heads, head_dim)
+        context_vec = (attn_weights @ values).transpose(1, 2)
+
+        # Combine heads, where self.d_out = self.num_heads * self.head_dim
+        context_vec = context_vec.reshape(b, num_tokens, self.d_out)
+        context_vec = self.out_proj(context_vec)  # optional projection
+
+        return context_vec
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, emb_dim):
+        super().__init__()
+        self.eps = 1e-5
+        self.scale = nn.Parameter(torch.ones(emb_dim))
+        self.shift = nn.Parameter(torch.zeros(emb_dim))
+
+    def forward(self, x):
+        mean = x.mean(dim=-1, keepdim=True)
+        var = x.var(dim=-1, keepdim=True, unbiased=False)
+        norm_x = (x - mean) / torch.sqrt(var + self.eps)
+        return self.scale * norm_x + self.shift
+
+
+class GELU(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return (
+            0.5
+            * x
+            * (
+                1
+                + torch.tanh(
+                    torch.sqrt(torch.tensor(2.0 / torch.pi))
+                    * (x + 0.044715 * torch.pow(x, 3))
+                )
+            )
+        )
+
+
+class FeedForward(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
+            GELU(),
+            nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.att = MultiHeadAttention(
+            d_in=cfg["emb_dim"],
+            d_out=cfg["emb_dim"],
+            context_length=cfg["context_length"],
+            num_heads=cfg["n_heads"],
+            dropout=cfg["drop_rate"],
+            qkv_bias=cfg["qkv_bias"],
+        )
+        self.ff = FeedForward(cfg)
+        self.norm1 = LayerNorm(cfg["emb_dim"])
+        self.norm2 = LayerNorm(cfg["emb_dim"])
+        self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
+
+    def forward(self, x):
+        # Shortcut connection for attention block
+        shortcut = x
+        x = self.norm1(x)
+        x = self.att(x)  # Shape [batch_size, num_tokens, emb_size]
+        x = self.drop_shortcut(x)
+        x = x + shortcut  # Add the original input back
+
+        # Shortcut connection for feed-forward block
+        shortcut = x
+        x = self.norm2(x)
+        x = self.ff(x)
+        x = self.drop_shortcut(x)
+        x = x + shortcut  # Add the original input back
+
+        return x
+
+
+class GPTModel(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
+        self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
+        self.drop_emb = nn.Dropout(cfg["drop_rate"])
+
+        self.trf_blocks = nn.Sequential(
+            *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
+        )
+
+        self.final_norm = LayerNorm(cfg["emb_dim"])
+        self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
+
+    def forward(self, in_idx):
+        batch_size, seq_len = in_idx.shape
+        tok_embeds = self.tok_emb(in_idx)
+        pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
+        x = tok_embeds + pos_embeds  # Shape [batch_size, num_tokens, emb_size]
+        x = self.drop_emb(x)
+        x = self.trf_blocks(x)
+        x = self.final_norm(x)
+        logits = self.out_head(x)
+        return logits
+
+
+import torch.nn.functional as F
+
+
+def generate_text_simple(
+    model,
+    idx,
+    max_new_tokens: int,
+    context_size: int,
+    temperature=1.0,
+    stream=False,
+    tokenizer=None,
+):
+    """
+    If stream=True: return a generator that yields decoded tokens one at a time.
+    If stream=False: return the full generated tensor.
+    """
+    if tokenizer is None:
+        raise ValueError("Tokenizer must be provided for decoding.")
+
+    def _gen():
+        nonlocal idx
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -context_size:]
+            with torch.no_grad():
+                logits = model(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+            yield tokenizer.decode(idx_next[0].tolist())
+
+    if stream:
+        return _gen()
+    else:
+        from loguru import logger
+
+        logger.info("stream=False")
+        # run through generator silently, but collect idx
+        for _ in _gen():
+            pass
+        return idx
+
+
+if __name__ == "__main__":
+
+    GPT_CONFIG_124M = {
+        "vocab_size": 50257,  # Vocabulary size
+        "context_length": 1024,  # Context length
+        "emb_dim": 768,  # Embedding dimension
+        "n_heads": 12,  # Number of attention heads
+        "n_layers": 12,  # Number of layers
+        "drop_rate": 0.1,  # Dropout rate
+        "qkv_bias": False,  # Query-Key-Value bias
+    }
+
+    torch.manual_seed(123)
+    model = GPTModel(GPT_CONFIG_124M)
+    model.eval()  # disable dropout
+
+    start_context = "Hello, I am"
+
+    tokenizer = tiktoken.get_encoding("gpt2")
+    encoded = tokenizer.encode(start_context)
+    encoded_tensor = torch.tensor(encoded).unsqueeze(0)
+
+    print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
+    print("\nInput text:", start_context)
+    print("Encoded input text:", encoded)
+    print("encoded_tensor.shape:", encoded_tensor.shape)
+
+    out = generate_text_simple(
+        model=model,
+        idx=encoded_tensor,
+        max_new_tokens=10,
+        context_size=GPT_CONFIG_124M["context_length"],
+    )
+    decoded_text = tokenizer.decode(out.squeeze(0).tolist())
+
+    print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}")
+    print("\nOutput:", out)
+    print("Output length:", len(out[0]))
+    print("Output text:", decoded_text)

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:384c50d29354dbc9ff3eac3795e8c5c9b0a4bffb4bf8de5184c38ac3f1cbe847
+size 702554279

tokenizer_config.json

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+{
+    "tokenizer_class": "TiktokenTokenizer",
+    "auto_map": {
+        "AutoTokenizer": "tiktoken.TiktokenTokenizer"
+    },
+    "tiktoken_encoding": "gpt2",
+    "add_bos_token": false,
+    "add_eos_token": false
+}