Added model source.

README.md

@@ -1,3 +1,27 @@
----
-license: mit
----
+# Leaf
+
+An open source "prototype" AI model used for AI research.
+
+## About this project
+
+Leaf is an "experimental" AI model, utilising PyTorch.
+
+## Research
+
+With leaf we've been testing many capabilities of what AI could do. 
+
+Starting with a simple "embedded" python dataset, leaf uses only 2700 steps for training (the more steps, the  better it learns).
+
+**Training Data:** `
+{"this is a much longer text that will serve as a simple dataset for our tiny language model. The model will learn to predict the next character based on the previous characters in the sequence."}
+{"text": "This demonstrates the core idea behind training an autoregressive language model. The quick brown fox jumps over the lazy dog."}
+{"text": "A journey of a thousand miles begins with a single step. The early bird catches the worm. All that glitters is not gold. A stitch in time saves nine."}
+{"text": "Where there's a will, there's a way. Look before you leap. You can't make an omelette without breaking a few eggs. Practice makes perfect. Don't count your chickens before they hatch."}`
+
+However this result came with the following output: 
+
+`text that will serve`
+
+Then we used JSONL databases from the community, and unfortunatly this was the output:
+
+`rimetricE7tich then`

model.py

@@ -0,0 +1,267 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import json
+import os
+
+# --- Hyperparameters ---
+# These are the settings for our model. You can experiment with these values.
+batch_size = 32  # How many sequences to process in parallel
+block_size = 8  # Maximum context length for predictions
+max_iters = 3000  # Number of training iterations
+eval_interval = 300  # How often to evaluate the model
+learning_rate = 1e-2  # The learning rate for the optimizer
+device = 'cuda' if torch.cuda.is_available() else 'cpu'  # Use GPU if available
+eval_iters = 200  # Number of iterations for evaluation
+n_embd = 32  # The dimension of the token embeddings
+n_head = 4  # The number of attention heads in the Multi-Head Attention block
+n_layer = 4  # The number of Transformer blocks
+dropout = 0.0  # Dropout rate for regularization
+
+# --- Data Preparation ---
+# To use this code, you need to create a file named 'dataset.jsonl'
+# in the same directory as this script. Each line of the file should be a JSON object
+# with 'header' and 'formal_statement' keys, like the example you provided.
+file_path = 'dataset.jsonl'
+
+# Process the JSONL data from the file.
+corpus = ""
+try:
+    with open(file_path, 'r') as f:
+        for line in f:
+            data_point = json.loads(line)
+            # Combine the 'header' and 'formal_statement' fields.
+            # We add a newline character to separate the two parts of the text.
+            corpus += data_point['header'] + '\n' + data_point['formal_statement'] + '\n'
+except FileNotFoundError:
+    print(f"Error: The file '{file_path}' was not found. Please create it and add your data.")
+    exit()
+except json.JSONDecodeError:
+    print(f"Error: There was a problem parsing a line in '{file_path}'. Make sure each line is a valid JSON object.")
+    exit()
+except KeyError:
+    print(f"Error: A line in '{file_path}' does not have the 'header' or 'formal_statement' keys. Please check your JSONL file format.")
+    exit()
+
+# Check if the corpus is empty after loading the file.
+if not corpus:
+    print(f"Error: The corpus is empty. This could be because '{file_path}' is empty or contains no valid text.")
+    exit()
+
+# Here we create a simple character-level tokenizer.
+# The vocabulary consists of all unique characters in the text.
+chars = sorted(list(set(corpus)))
+vocab_size = len(chars)
+stoi = {ch: i for i, ch in enumerate(chars)}
+itos = {i: ch for i, ch in enumerate(chars)}
+# Fix the bug in the encode function. The loop variable was 's' instead of 'c'.
+encode = lambda s: [stoi[c] for c in s]
+decode = lambda l: ''.join([itos[i] for i in l])
+
+# Convert the entire text into a PyTorch tensor.
+data = torch.tensor(encode(corpus), dtype=torch.long)
+
+# Create a simple train/validation split.
+n = int(0.9 * len(data))
+train_data = data[:n]
+val_data = data[n:]
+
+# --- Helper Functions ---
+# This function gets a random batch of data from either the training or validation set.
+def get_batch(split):
+    data = train_data if split == 'train' else val_data
+    # Generate random starting indices for each sequence in the batch.
+    ix = torch.randint(len(data) - block_size, (batch_size,))
+    # Stack the sequences to create a batch.
+    x = torch.stack([data[i:i + block_size] for i in ix])
+    y = torch.stack([data[i + 1:i + block_size + 1] for i in ix])
+    x, y = x.to(device), y.to(device)
+    return x, y
+
+# This function is used to estimate the model's loss on both the train and validation sets.
+# It uses torch.no_grad() to make the process more efficient as we're not training.
+@torch.no_grad()
+def estimate_loss():
+    out = {}
+    model.eval()  # Set the model to evaluation mode.
+    for split in ['train', 'val']:
+        losses = torch.zeros(eval_iters)
+        for k in range(eval_iters):
+            X, Y = get_batch(split)
+            logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    model.train()  # Set the model back to training mode.
+    return out
+
+# --- The Self-Attention Mechanism ---
+# This is a single attention head.
+class Head(nn.Module):
+    def __init__(self, head_size):
+        super().__init__()
+        # Linear layers to project the input into key, query, and value vectors.
+        self.key = nn.Linear(n_embd, head_size, bias=False)
+        self.query = nn.Linear(n_embd, head_size, bias=False)
+        self.value = nn.Linear(n_embd, head_size, bias=False)
+        # A buffer to store a lower-triangular matrix, which prevents future tokens from
+        # "seeing" past tokens (decoder-style attention).
+        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+        # Dropout layer for regularization.
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        B, T, C = x.shape
+        k = self.key(x)  # (B, T, head_size)
+        q = self.query(x)  # (B, T, head_size)
+        
+        # Compute the affinity scores (weights).
+        # (q @ k.transpose(-2, -1)) is matrix multiplication of q and k transpose.
+        wei = q @ k.transpose(-2, -1) * C**-0.5  # (B, T, head_size) @ (B, head_size, T) -> (B, T, T)
+        # Apply the lower-triangular mask to enforce causality.
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
+        # Apply softmax to get the attention weights.
+        wei = F.softmax(wei, dim=-1)
+        self.dropout(wei)
+
+        v = self.value(x)  # (B, T, head_size)
+        out = wei @ v  # (B, T, T) @ (B, T, head_size) -> (B, T, head_size)
+        return out
+
+# This combines multiple attention heads in parallel.
+class MultiHeadAttention(nn.Module):
+    def __init__(self, num_heads, head_size):
+        super().__init__()
+        # Create a list of `Head` modules.
+        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
+        # A final linear layer to project the concatenated output of all heads.
+        self.proj = nn.Linear(num_heads * head_size, n_embd)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        # Concatenate the output from each head.
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        out = self.dropout(self.proj(out))
+        return out
+
+# This is a simple feed-forward network.
+class FeedFoward(nn.Module):
+    def __init__(self, n_embd):
+        super().__init__()
+        # A simple linear-ReLU-linear stack.
+        self.net = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),
+            nn.ReLU(),
+            nn.Linear(4 * n_embd, n_embd),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+# This is a single Transformer block, composed of Multi-Head Attention and a Feed-Forward network.
+class TransformerBlock(nn.Module):
+    def __init__(self, n_embd, n_head):
+        super().__init__()
+        head_size = n_embd // n_head
+        # The attention mechanism.
+        self.sa = MultiHeadAttention(n_head, head_size)
+        # The feed-forward network.
+        self.ffwd = FeedFoward(n_embd)
+        # Layer normalization layers.
+        self.ln1 = nn.LayerNorm(n_embd)
+        self.ln2 = nn.LayerNorm(n_embd)
+
+    def forward(self, x):
+        # Apply self-attention with a residual connection and layer normalization.
+        x = x + self.sa(self.ln1(x))
+        # Apply feed-forward with another residual connection and layer normalization.
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+# --- The Main Language Model ---
+class LanguageModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # A token embedding table: each integer token gets a vector representation.
+        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
+        # A positional embedding table: each position gets a vector representation.
+        self.position_embedding_table = nn.Embedding(block_size, n_embd)
+        # A sequence of Transformer blocks.
+        self.blocks = nn.Sequential(*[TransformerBlock(n_embd, n_head) for _ in range(n_layer)])
+        # A final layer normalization.
+        self.ln_f = nn.LayerNorm(n_embd)
+        # A linear layer to project the final embeddings to the vocabulary size.
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+
+        # Get token embeddings and positional embeddings.
+        tok_emb = self.token_embedding_table(idx)  # (B, T, C)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=device))  # (T, C)
+        # Add them together to get the final embeddings.
+        x = tok_emb + pos_emb  # (B, T, C)
+        # Pass through the Transformer blocks.
+        x = self.blocks(x)
+        x = self.ln_f(x)
+        # Project to the vocabulary size.
+        logits = self.lm_head(x)  # (B, T, vocab_size)
+
+        loss = None
+        if targets is not None:
+            # Reshape for cross-entropy loss calculation.
+            B, T, C = logits.shape
+            logits = logits.view(B * T, C)
+            targets = targets.view(B * T)
+            loss = F.cross_entropy(logits, targets)
+
+        return logits, loss
+
+    # A function to generate text.
+    def generate(self, idx, max_new_tokens):
+        # idx is (B, T) tensor of indices in the current context.
+        for _ in range(max_new_tokens):
+            # Crop idx to block_size, as the model has a limited context.
+            idx_cond = idx[:, -block_size:]
+            # Get predictions.
+            logits, loss = self(idx_cond)
+            # Focus only on the last time step.
+            logits = logits[:, -1, :]
+            # Apply softmax to get probabilities.
+            probs = F.softmax(logits, dim=-1)
+            # Sample from the distribution.
+            idx_next = torch.multinomial(probs, num_samples=1)
+            # Append the new token to the sequence.
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+# --- Training and Generation ---
+model = LanguageModel()
+m = model.to(device)
+
+# Create a PyTorch optimizer.
+optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
+
+# Main training loop.
+for iter in range(max_iters):
+    # Every few iterations, evaluate the loss on both splits.
+    if iter % eval_interval == 0:
+        losses = estimate_loss()
+        print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
+
+    # Sample a batch of data.
+    xb, yb = get_batch('train')
+
+    # Forward pass: compute loss.
+    logits, loss = model(xb, yb)
+    # Backward pass: compute gradients.
+    optimizer.zero_grad(set_to_none=True)
+    loss.backward()
+    # Update the model parameters.
+    optimizer.step()
+
+# --- Generate new text from the trained model ---
+context = torch.zeros((1, 1), dtype=torch.long, device=device)
+generated_text_indices = m.generate(context, max_new_tokens=20)
+print("\nGenerated text:")
+print(decode(generated_text_indices[0].tolist()))