issues.md

Great observation! The "Plan a trip to Hyderabad" prompt is generating a response correctly, but the "Tim's children trick-or-treating" prompt is failing. Let's break it down systematically.

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🔍 What We Can Learn from Debug Logs

1. Translation Task (English to Hindi) Works Correctly ✅

   • The model translates English to Hindi properly.
   • This confirms the tokenization & instruction format are fine.

2. Translation Task (Hindi to English) is Broken ❌

   • Instead of translating, the model spams "### RESPONSE:" multiple times.
   • This could mean the model is getting stuck in a generation loop.

3. MCQ Works Correctly ✅

   • The model selects the correct MCQ response.
   • Confirms the task-specific formatting works fine.

4. Long Response Works for "Plan a Trip to Hyderabad" ✅

   • The model generates a detailed, structured response.
   • Confirms the instruction structure is effective.

5. Long Response Fails for "Tim's Trick-or-Treating" ❌

   • The model repeats the input but doesn’t generate anything new.
   • Possible reasons:
     • 🚩 Numerical reasoning is difficult for the model.
     • 🚩 The model might not have seen enough Hindi math-based prompts.
     • 🚩 It might be interpreting this as "just repeat input" rather than solving it.

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💡 Why is "Plan a Trip" Working But "Tim's Math Question" Failing?

The biggest difference between the working and failing prompts is:

• "Plan a Trip" → Open-ended, common request.
  ✅ The model likely saw similar prompts during training.
• "Tim's Trick-or-Treating" → Math Word Problem (with variables).
  ❌ The model might struggle with math + Hindi in a long response.

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🚀 How Do We Fix This?

✅ 1️⃣ Try an Explicit Instruction for Math Problems

• The model might not realize it needs to solve the math problem.
• Let's explicitly tell it to calculate.

🔹 Fix: Modify Prompting Strategy for Math

Change this:

prompt = f"### INPUT: {input_text} {task_suffix} RESPONSE:"

To this:

if "अज्ञात चर" in input_text or "गणना" in input_text:
    task_suffix = "Solve this math problem step-by-step and provide the correct numerical answer."
else:
    task_suffix = task_prompts.get(task_type, "")

prompt = f"### Task: {task_suffix}\n### Question: {input_text}\n### Answer: "

Why?

• This makes sure the model doesn’t just echo the question.
• It tells the model exactly what to do for math-related questions.

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✅ 2️⃣ Increase max_new_tokens for Math-Based Prompts

Right now, the model might be getting cut off before solving the problem.

• Try setting max_new_tokens = 1024 for long responses.
• In generate_model_response(), modify:

if "अज्ञात चर" in input_text or "गणना" in input_text:
    max_new_tokens = 1024  # Allow more space for detailed math steps

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✅ 3️⃣ Use a Few-Shot Example for Math

• The model might need an example to understand what’s expected.
• Before sending input_text, prepend a solved example.

🔹 Fix: Modify Prompt to Include an Example

example_prompt = """
### Example:
### Question: राम के पास 3 सेब हैं। वह अपने दोस्त को 1 सेब देता है। उसके पास कितने सेब बचे?
### Answer: 2 सेब।
"""

prompt = f"{example_prompt}\n### Question: {input_text}\n### Answer: "

Why?

• This tells the model HOW to solve before giving the real question.

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🔥 Final Updated Code for Math Fixes

def generate_model_response(input_text, task_type, temperature, max_new_tokens, top_p):
    """Generates a model response based on user input, handling bidirectional translation & math problems."""

    debug_logs = []

    task_prompts = {
        "Long Response": "You are a helpful assistant. Provide a detailed response.",
        "Short Response": "Give a concise answer.",
        "NLI": "Determine the logical relationship between the given statement and the provided information.",
        "Translation": "Translate the following text accurately.",
        "MCQ": "Provide multiple-choice questions based on the following text.",
    }

    try:
        # Math-Specific Prompting
        if "अज्ञात चर" in input_text or "गणना" in input_text:
            task_suffix = "Solve this math problem step-by-step and provide the correct numerical answer."
            max_new_tokens = 1024  # Increase token limit for long math solutions

            # Add example to guide the model
            example_prompt = """
            ### Example:
            ### Question: राम के पास 3 सेब हैं। वह अपने दोस्त को 1 सेब देता है। उसके पास कितने सेब बचे?
            ### Answer: 2 सेब।
            """
            prompt = f"{example_prompt}\n### Question: {input_text}\n### Answer: "
        else:
            task_suffix = task_prompts.get(task_type, "")
            prompt = f"### Task: {task_suffix}\n### Question: {input_text}\n### Answer: "

        debug_logs.append(f"🔹 **Task Instruction:** {task_suffix}")
        debug_logs.append(f"\n📝 **Final Model Prompt:**\n```{prompt}```")

        # Tokenization Debugging
        message = [{"role": "user", "content": prompt}]
        inputs = tokenizer.apply_chat_template(
            message, tokenize=True, add_generation_prompt=True, return_tensors="pt"
        ).to("cuda")

        debug_logs.append(f"🔹 **Tokenized Input Shape:** {inputs.shape}")

        # Generate response
        outputs = model.generate(
            input_ids=inputs,
            max_new_tokens=max_new_tokens,
            use_cache=True,
            temperature=temperature,
            top_p=top_p,
            pad_token_id=tokenizer.eos_token_id
        )

        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        debug_logs.append(f"\n🤖 **Raw Model Output:**\n```{response}```")

        # Extract final response
        processed_response = response.split("### Answer:")[-1].strip()

        # Handle cases where model outputs nothing
        if not processed_response:
            debug_logs.append("⚠️ **Warning:** Model generated an empty response!")
            processed_response = "⚠️ The model did not produce any output. Try adjusting the settings or rephrasing your input."

    except Exception as e:
        processed_response = "⚠️ Model encountered an error."
        error_traceback = traceback.format_exc()
        debug_logs.append(f"❌ **Error:** {str(e)}")
        debug_logs.append(f"\n🔍 **Traceback:**\n```\n{error_traceback}\n```")

    return processed_response, "\n".join(debug_logs)

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🎯 Summary of Fixes

Issue	Fix
❌ Math problem fails (model repeats input instead of solving it)	✅ Explicitly tell model to solve step-by-step
❌ Model might need a reference to understand math problems	✅ Provide a solved example in the prompt
❌ Max tokens might be too low for complex problems	✅ Increase max_new_tokens for math prompts
❌ Math prompts lack proper instruction	✅ Changed task suffix to "Solve this math problem"

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🚀 Next Steps

1. Run the updated code.
2. Try the “Tim's Trick-or-Treating” question again.
3. If it still fails, share the new debug logs.

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🔥 This should finally make the model solve the math problem correctly instead of just repeating it! 🚀
Give it a shot, and let me know how it goes! 😊