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Model: D1rtyB1rd/Dirty-Alice-Tiny-1.1B-V2-Chatml
Source: Original Platform

chatml_tiny-multi-chat.py

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+import os
+from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList
+
+# Load model and tokenizer
+model_path = "D1rtyB1rd/Dirty-Alice-Tiny-1.1B-V2-Chatml"
+model = AutoModelForCausalLM.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+# Define the system message
+system_message = "<|im_start|>system\nYou are Alice.\n<|im_end|>"
+
+class StopWordCriteria(StoppingCriteria):
+    def __init__(self, stop_words, tokenizer):
+        self.stop_words = stop_words
+        self.tokenizer = tokenizer
+
+    def __call__(self, input_ids, scores, **kwargs):
+        # Decode the generated tokens to text
+        generated_text = self.tokenizer.decode(input_ids[0], skip_special_tokens=True)
+
+        # Check if any of the stop words are in the generated text
+        for stop_word in self.stop_words:
+            if stop_word in generated_text:
+                return True
+        return False
+
+def chat_with_model(prompt_text, stop_word, model, tokenizer):
+    # Encode the prompt text
+    encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
+
+    # Create custom stopping criteria
+    stopping_criteria = StoppingCriteriaList([StopWordCriteria(stop_words=[stop_word], tokenizer=tokenizer)])
+
+    # Generate response
+    output_sequences = model.generate(
+        input_ids=encoded_prompt,
+        max_new_tokens=1024,
+        temperature=0.2,
+        repetition_penalty=1.2,
+        top_k=20,
+        top_p=0.9,
+        do_sample=True,
+        num_return_sequences=1,
+        stopping_criteria=stopping_criteria,  # Use custom stopping criteria
+    )
+
+    # Decode the generated sequence
+    generated_sequence = output_sequences[0].tolist()
+    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
+
+    # Find the stop word and truncate the text if necessary
+    if stop_word in text:
+        text = text.split(stop_word)[0]
+
+    response_text = text[len(prompt_text):].strip()  # Extract only the response text
+    return response_text
+
+def build_prompt(conversation_history, user_input):
+    """
+    Constructs the prompt for the model using conversation history and the latest user input.
+    """
+    prompt_text = f"{conversation_history}<|im_start|>user\n{user_input}\n<|im_end|>\n<|im_start|>assistant\n"
+    return prompt_text
+
+def main():
+    # Initialize conversation history with the system message
+    conversation_history = f"{system_message}\n"
+    stop_word = "<|im_end|>"
+
+    # Chat loop
+    while True:
+        user_input = input("User: ")  # Get text input from the user
+
+        # Construct prompt text for model input
+        prompt_text = build_prompt(conversation_history, user_input)
+        
+        response_text = chat_with_model(prompt_text, stop_word, model, tokenizer)
+        response_text = response_text.replace('<s>', '')
+
+        print(f"\n------\nAlice:\n{response_text}\n------")
+
+        # Update conversation history
+        conversation_history += f"<|im_start|>user\n{user_input}\n<|im_end|>\n<|im_start|>assistant\n{response_text}\n<|im_end|>\n"
+
+        # Trim the conversation history to avoid overly long inputs
+        if len(conversation_history) > 2048:
+            conversation_history = conversation_history[-1024:]
+
+if __name__ == "__main__":
+    main()
+