Rename tg.py to tg.ggml

tg.ggml

@@ -0,0 +1,57 @@
+// 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: 64;
+  }
+
+  // Encoder layers
+  encoder {
+    type: lstm;
+    units: 128;
+    num_layers: 2;
+  }
+
+  // Decoder layers
+  decoder {
+    type: gru;
+    units: 64;
+    num_layers: 1;
+  }
+
+  // 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
+train {
+  // ... (Define training data and hyperparameters)
+}
+
+// Inference parameters
+inference {
+  // ... (Define input and output behavior)
+}

tg.py

@@ -1,41 +0,0 @@
-def generate_text(seed_word, max_length=100):
-  """
-  Generates text using a simple rule-based system.
-
-  Args:
-      seed_word: The word to start the generation.
-      max_length: The maximum length of the generated text.
-
-  Returns:
-      A string of generated text.
-  """
-
-  # Define a dictionary of word pairs (key, value) where the key is a word and the value is a list of possible next words
-  word_pairs = {
-      "hello": ["world", "there"],
-      "world": ["is", "beautiful"],
-      "beautiful": ["day", "morning"],
-      "day": ["the", "end"],
-      "the": ["quick", "brown"],
-      "brown": ["fox", "jumps"],
-      "fox": ["over", "the"],
-      "jumps": ["lazy", "dog"],
-      "lazy": ["dog", "sleeps"],
-      "dog": ["all", "day"],
-      "sleeps": ["all", "night"],
-      "night": ["and", "dreams"],
-      "dreams": ["of", "chasing"],
-      "chasing": ["mice", "again"],
-      "again": ["the", "end"]
-  }
-
-  text = seed_word
-  while len(text.split()) < max_length and text[-1] in word_pairs:
-    next_words = word_pairs[text[-1]]
-    text += " " + random.choice(next_words)
-  return text
-
-# Example usage
-seed_word = "hello"
-generated_text = generate_text(seed_word)
-print(generated_text)