Rename tg.ggml to tg.py

tg.ggml

@@ -1,69 +0,0 @@
-// Model definition
-model MyModel {
-  // Input features
-  input text: string;
-
-  // Tokenization layer
-  tokenizer {
-    type: SentencePiece;
-    vocab_size: 256; // Adjust based on dataset
-  }
-
-  // Embedding layer
-  embeddings {
-    dim: 128;
-  }
-
-  // Encoder layers
-  encoder {
-    type: lstm;
-    units: 128;
-    num_layers: 2;
-    dropout: 0.2;  // Add dropout for regularization
-  }
-
-  // Decoder layers
-  decoder {
-    type: gru;
-    units: 64;
-    num_layers: 2;
-    dropout: 0.1;  // Add dropout for regularization
-  }
-
-  // Attention mechanism
-  attention {
-    type: scaled_dot_product;
-  }
-
-  // Output layer
-  output {
-    type: dense;
-    units: vocab_size;
-  }
-
-  // Loss function
-  loss {
-    type: softmax_cross_entropy;
-  }
-
-  // Optimizer
-  optimizer {
-    type: adam;
-    learning_rate: 0.001;
-  }
-}
-
-// Training parameters (in train.json)
-{
-  "data_path": "path/to/your/training_data.txt",
-  "batch_size": 32,
-  "epochs": 10,
-  "use_scheduled_sampling": true,  // Enable scheduled sampling
-  "clip_gradients": 5.0,  // Add gradient clipping to prevent exploding gradients
-  "use_batch_norm": true  // Enable batch normalization
-}
-
-// Inference parameters
-inference {
-  // ... (Define input and output behavior)
-}

tg.py

@@ -0,0 +1,36 @@
+import tensorflow as tf
+
+# Improved training corpus with more data and variety
+corpus = """
+The quick brown fox jumps over the lazy dog.
+Machine learning is the study of algorithms that can learn from data.
+Natural language processing is a subfield of artificial intelligence concerned with the interactions between computers and human language.
+Deep learning is a class of machine learning algorithms that use multiple layers of artificial neural networks to learn from data.
+"""
+
+# Tokenization and vectorization (one-hot encoding for simplicity)
+tokens = corpus.split()
+vocab_size = len(set(tokens))
+token_vectors = tf.one_hot(tf.strings.lookup(tokens, tf.constant([0] * vocab_size)), vocab_size)
+
+# Model architecture with embedding, LSTM layers, and dense output
+model = tf.keras.Sequential([
+    tf.keras.layers.Embedding(vocab_size, 8),  # Embedding layer
+    tf.keras.layers.LSTM(16, return_sequences=True),  # Recurrent LSTM layer
+    tf.keras.layers.LSTM(16),
+    tf.keras.layers.Dense(vocab_size)  # Output layer
+])
+
+# Compile the model with categorical crossentropy loss and Adam optimizer
+model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+# Train the model for a limited number of epochs (adjust as needed)
+model.fit(token_vectors[:-1], token_vectors[1:], epochs=20)
+
+# Generate text starting with "The"
+prompt_vector = tf.one_hot(tf.constant([tokens.index("The")]), vocab_size)
+for i in range(10):
+  prediction = model.predict(tf.expand_dims(prompt_vector, axis=0))
+  predicted_index = tf.argmax(prediction, axis=1).numpy()[0]
+  prompt_vector = tf.concat([prompt_vector, tf.one_hot([predicted_index], vocab_size)], axis=0)
+  print(tokens[predicted_index], end=" ")