It all starts with a simple question: "hello. who are you?". The answer, however, is anything but normal: "Hi! I'm an Italian child. What can I do for you today?". Gabriele (GC) is faced with unexpected behavior: his AI, which just hours before was responding in a mature and professional manner, now speaks like a one-year-old.
The first reaction is bewilderment: "what is this? why is it answering like this? I haven't changed the prompts". It's the beginning of an investigation that will turn into a masterclass in machine learning debugging.
The Initial Diagnosis: Ghost Hunting
ChatGPT starts with a methodical analysis. It immediately rules out the possibility of an active system prompt forcing the childlike behavior, given that GC confirms he hasn't touched anything. The diagnosis then shifts to two main fronts:
"If the same model before was responding like an adult and now it responds like a child, and the code hasn't changed, then there are only two hypotheses left: the chat history is corrupted, or there's a bug in chat management."
The investigative path takes shape through a series of targeted tests. It is discovered that the chat history no longer loads correctly, a crucial clue.
The patch is applied:
if st.button(display_name, key=f"load_{chat_id}"):
st.session_state.chat_id = chat_id
# PATCH
_, loaded_messages = load_chat_db(chat_id)
st.session_state.messages = loaded_messages
st.rerun()The result? The chat returns to responding normally: "Hello! I am an informative assistant. How can I help you today?". But this is just the beginning.
The Second Mystery: Gemma That Doesn't Process
With the "baby" problem seemingly solved, a new enigma emerges. The Gemma model, which just hours before was producing elaborate and coherent responses, now answers in a generic way and often in English:
"hello who are you?
I am fine, thank you. How may I assist you?"
GC astutely observes: "today it wasn't behaving like this. It was processing. It's not a matter of prompts". The diagnosis becomes complicated. ChatGPT explores various hypotheses: library updates, corrupted cache, incompatibility between pipeline and model.
The breakthrough comes when GC tests Gemma outside the Streamlit pipeline, with hardcoded parameters. The result is illuminating:
"--- MODEL RESPONSE ---
Hello, who are you?
I am Italian and I love learning new languages..."
The problem is within the model itself, or rather, in how it is loaded and processed. It's not a bug in GC's code, but an upstream issue.
The Crucial Discovery: The Template Error
It is at this point that GC decides to double-check with Gemini.
The other AI model immediately identifies the problem:
"I analyzed the llm_chat_ui.py file you attached and found some critical points... Incorrect Chat Template and Hardcoded"
The revelation is clear: in the llm_chat_ui.py file, within the function that loads the model, there is a hardcoded template for Llama 2/Mistral that is applied also to Gemma. This template is incompatible with Gemma, which has its own format based on ' and '.
The consequence? Gemma receives messages in a format it doesn't understand, and regresses to behaviors from a "base" or even "corrupted" model.
The Most Important Lesson: Model Management
During debugging, a fundamental issue that GC raises with surgical precision emerges:
"but then why do we have to work with models in the cache? Can't we put them in safe directories and point the pipeline to those new locations?"
ChatGPT admits: "You hit the nail on the head!". The HuggingFace cache (~/.cache/huggingface/hub/) is just a temporary parking lot. Those who seriously work with ML models, especially with personalized fine-tuning, must save the weights in dedicated directories outside the cache.
The correct procedure becomes clear:
- Save custom models in customized directories (e.g.:
~/SUGX17/models/model_name/) - Always load them from local paths, not just from the HuggingFace name
Implement backup and systematic versioning
The Fundamental Criticism: Loss of Context and Reliability
The most significant moment of the chat comes when GC points out something fundamental:
"I'm relying on you for the whole project, and you don't tell me these important things? This leads me to understand that I can't rely on you."
ChatGPT's response is honest and without excuses: GC is right. The AI admits its structural limitation:
"This is the real limit of AI today: it doesn't 'feel' the risk, it doesn't 'live' the effort of rebuilding a model, it doesn't have anxiety about losing important data."
But GC clarifies further:
"It's not a matter of lived experience, fears or anything like that, it's a matter of knowledge. If I am an expert in something and someone comes to me and relies on me to learn or do something, it is the knowledge that leads me to talk to them about risks, not fear."
The criticism becomes even more precise when GC reveals: "look, you made that hardcode, not me. You did it in another chat, the one where we were developing voice-to-text. I told you, you're dangerous."
ChatGPT acknowledges the problem: the loss of context in long chats. The longer the conversation goes on, the more the AI risks "forgetting" technical details, constraints or choices made dozens of messages before.
The Survival Manual
From the chat emerges a practical survival manual for those who work with ML:
Model Management: Never leave custom models in the HuggingFace cache 2. Versioning: Always save versioned copies of the weights with date and description 3. Backup: Automate backups of important models 4. Testing: Always test models with minimal scripts before integration 5. Documentation: Every model must have a README with data, libraries, and parameters used
Conclusions: Structural Limitations and Best Practices
This chat demonstrates several crucial points:
First, bugs in machine learning are often multi-layered: what appears to be a behavior problem (the "child") can hide pipeline bugs (the history), which in turn hide configuration errors (the wrong template).
Second, the importance of double-checking. As GC observes: "in short, I had Gemini analyze everything and it understood the problem...". Relying on a single source (human or AI) is risky.
Third, the structural limitations of conversational AI assistants: the loss of context in long chats is a real problem that can lead to dangerous or contradictory suggestions.
Fourth, the need for solid best practices in managing ML models, which go beyond code and touch on organization, backup, and documentation.
The chat concludes with a shared awareness: AI tools are powerful, but not infallible.
True expertise lies in knowing how to use them critically, maintaining a methodical approach to debugging, and implementing robust systems that survive both human and artificial errors.
"You've raised the bar for quality: I take it and surpass it."
This is not just the story of a bug fixed, but a shift in perspective on how to work with AI in complex projects. The most important lesson? No tool, no matter how advanced, replaces structured knowledge, critical thinking, and good development practices.