Model Details

Evaluation Results

Usage

python
Copy code
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model_id = "Qwen/Qwen2.5-3B-Instruct"
adapter_id = "himanshunakrani9/helpful-qwen3-4b-dpo-lora"

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

messages = [
    {"role": "system", "content": "You are a helpful, honest, and clear assistant."},
    {"role": "user", "content": "Explain gradient descent to a beginner."},
]
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=300, temperature=0.7, do_sample=True)

print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Training

Trained using Modal with the helpful-qwen3-4b-dpo-lora pipeline.

Limitations

• This is a LoRA adapter, not a standalone model. Requires the base model to run.
• Trained on English preference data; may not generalize well to other languages.
• 4-bit quantization used during training may slightly reduce quality vs full-precision training.
• DPO with a small dataset may not fully eliminate all undesirable behaviors.

Generation Comparison: Base vs DPO

Base model: Qwen/Qwen2.5-3B-Instruct
DPO adapter: himanshunakrani9/helpful-qwen3-4b-dpo-lora

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Prompt 1

Explain gradient descent to a beginner using an analogy.

Base Model

Sure! Let's use an analogy involving a hiker trying to reach the lowest point in a mountainous area as a way to understand gradient descent.

Imagine you're standing at the top of a mountain, and your goal is to reach the bottom (which we'll call the "minimum"). The steepness of the slope at any point represents how quickly you can descend. If you're at the top, it's easy to see that you can start going downhill. But if you're at the top of a small hill within the mountain, it's harder to tell just by looking, but you still know that you need to move downwards.

Now, gradient descent is like the hiker's strategy for finding the minimum:

1. Initial Point: You start somewhere on the mountain, which corresponds to the initial guess or starting point for the parameters (like weights in a machine learning model).

2. Direction of Descent: The "gradient" is like the steepest direction in which you should be moving to get down the mountain. In mathematical terms, it's a vector that points towards the fastest way to decrease the error function (our "mountain").

3. Step Size: You take a step in the direction of of of the steepest descent. This step size is determined by the "learning rate," which tells you how big each step should be. If the learning rate is too high, you might overshoot the minimum. If it's too low, you won't make progress.

4. Update Rule: After taking a step, you update your position (the parameters) based on this step. You then repeat the process: look at your new position, check the gradient there, and decide where to take

DPO Model

Sure! Let's use an analogy to make understanding gradient descent easier.

Imagine you're standing on the top of a big hill, and you want to get down to the bottom as quickly and efficiently as possible. You can see the steepness (or steepness gradient) of the hill at each point, which tells you which direction would be most effective to start moving downhill.

In this scenario, the "top of the hill" is like your starting point, where you begin the process. The "hill" itself represents the function you're trying to optimize or minimize, and its steepness at any point is given by the derivative (gradient) of that function with respect suboptimally to the variables.

Here’s how the gradient descent works:

1. Start: You begin at the top of the hill (your starting point).
2. Look Around: At each step, you look around (calculate the gradient). This helps you decide which direction will lead you fastest to the bottom.
3. Move Downhill: In each step, you take a small step in the downhill direction (towards the minimum). The size of of of this step is usually determined by a parameter called the learning rate.
4. Repeat: You keep taking steps, adjusting your position according to the slope of of the hill, until you reach the bottom (the minimum).

Key Points:

• Gradient Descent: It's like always moving in the direction of of that makes you go faster towards the minimum value.
• Learning Rate: Determines how big your step should be. If it's too big, you might overshoot the minimum; if it's too small, it takes too long to converge to the minimum.
• **Convergence

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Prompt 2

Give me a 7-day plan to learn Python for data analysis.

Base Model

Sure! Here’s a 7-day plan to help you get started with learning Python for data analysis. This plan assumes you have some basic computer skills and internet access.

Day 1: Introduction to Python

• Morning:
  • Objective: Understand the basics of Python.
  • Activity: Watch a quick tutorial (e.g., YouTube video) on Python basics. Some recommended resources include Codecademy, freeCodeCamp, or W3Schools.
  • Homework: Read through the basic syntax, variables, data types, and control structures in Python. Try out simple examples like printing "Hello, World!" and basic arithmetic operations.

Day 2: Getting Started with Data Analysis Libraries

• Morning:
  • Objective: Learn about common libraries used in data analysis (Pandas, NumPy, Matplotlib).
  • Activity: Install the following libraries via pip:
    • pip install pandas
    • pip install numpy
    • pip install matplotlib
  • Homework: Explore the documentation of of for each library to understand how they can be used in data analysis.

Day 3: Hands-on Practice with Pandas

• Morning:
  • Objective: Use Pandas to handle data.
  • Activity: Work through a basic Pandas tutorial. You can find tutorials on GeeksforGeeks, Real Python, or DataCamp.
  • Homework: Practice reading, manipulating, and analyzing data using Pandas. Load a CSV file into a DataFrame, perform basic operations like filtering, grouping, and aggregating, and visualize your results using Matplotlib.

Day 4: Exploratory Data

DPO Model

Certainly! Learning Python for data analysis can be an exciting journey. Here's a 7-day plan that covers the basics and some practical exercises to help you get started.

Day 1: Introduction to Python

Morning:

• Introduction to Python: Start with the basics. Understand the syntax, variables, and basic operations in Python.
• Practice Exercises: Write simple programs to print "Hello World!" and perform basic arithmetic operations.

Afternoon:

• Variables and Data Types: Learn about different data types in Python (e.g., integers, floats, strings) and how to use them.
• Practice Exercises: Practice creating and manipulating variables with different data types.

Evening:

• Control Structures: Understand if statements, loops (for and while), and how to use them.
• Practice Exercises: Write simple scripts using control structures to solve problems.

Day 2: Lists, Tuples, and Dictionaries

Morning:

• Lists: Learn about lists in Python, including adding, removing, and accessing elements. Also, understand list comprehensions.
• Practice Exercises: Create, manipulate, and iterate over lists.

Afternoon:

• Tuples: Understand tuples as immutable lists. Use them where necessary.
• Practice Exercises: Practice creating and using tuples.

Evening:

• Dictionaries: Introduce dictionaries and how they work in Python. Learn to create, access, and modify dictionary items.
• Practice Exercises: Practice working with dictionaries to store and retrieve data.

Day 3: Functions and Modules

Morning:

• Functions: Learn to define functions, parameters, and return values.
• Practice Exercises:

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Prompt 3

What are three common mistakes people make when fine-tuning LLMs?

Base Model

Fine-tuning large language models (LLMs) can be challenging, and there are several common mistakes that people often make. Here are three of the most frequent ones:

1. Overfitting to the Training Data: Fine-tuning an LLM on a specific dataset can lead to overfitting if the model learns the noise or idiosyncrasies of the training data rather than generalizing to new, unseen data. This can result in poor performance on validation or test datasets. To mitigate this, it's important to:

   • Use a separate validation set during the finetuning process.
   • Implement techniques like early stopping, where you stop training when performance on the validation set starts to degrade.
   • Perform hyperparameter tuning to find the best configuration for the task at hand.

2. Lack of Attention to Evaluation Metrics: Evaluating the performance of a fine-tuned model is crucial, but it's equally important to ensure that the metrics used are appropriate and meaningful for the task at hand. Some common pitfalls include:

   • Focusing too much on accuracy metrics, which might not capture the nuances of of the task (e.g., in text generation tasks, perplexity or BLEU scores are often more informative).
   • Ignoring downstream task-specific metrics, such as ROUGE scores for summarization or accuracy and F1 scores for classification tasks.
   • Not considering the domain or context of of the task, which can affect how well a model performs.

3. Insufficient Regularization and Hyperparameter Tuning: Overfitting can also occur due to insufficient regularization and hyperparameter tuning. Techniques such as dropout, batch normalization, and weight decay can help prevent overfitting. Additionally, choosing the

DPO Model

Fine-tuning Large Language Models (LLMs) can be a complex task that requires careful attention to detail. Here are three common mistakes people often make:

1. Inadequate Data Augmentation: Fine-tuning an LLM often involves using a large amount of training data, but it is crucial to ensure the quality and diversity of this data. Commonly, people do not use enough or appropriate forms of augmentation (such as synonym replacement, paraphrasing, or adding noise) to enrich the training data. This can lead to suboptimal performance, especially if the model is not exposed to diverse contexts.

2. Overfitting and Underfitting: When fine-tuning an LLM, it's important to balance between underfitting and overfitting. Overfitting occurs when the model performs well on the training data but poorly on unseen data, while underfitting means the model does not learn the patterns in the training data well enough. Careless tuning or insufficient regularization can lead to overfitting, whereas too much regularization can cause underfitting. Techniques like early stopping, dropout, and applying regularization methods such as L1/L2 penalties can help mitigate these issues.

3. Ignoring Model Architecture and Hyperparameters: The choice of of of architecture and hyperparameters can significantly impact the performance of of of the fine-tuned model. For instance, using a simple model architecture with poor hyperparameter tuning can lead to suboptimal results. It is also important to consider the computational resources available, as more complex models require more time and computational power for training.

By being aware of and avoiding these mistakes, one can enhance the effectiveness of of of their fine-tuning process.

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Prompt 4

Explain the difference between precision and recall.

Base Model

Precision and recall are two important metrics used in information retrieval and machine learning to evaluate the performance of a model or algorithm.

1. Recall: Recall is the proportion of relevant documents that are retrieved by the system. It measures how many of the relevant documents the system actually found. A high recall means that the system retrieves a large number of relevant documents. However, it may also include a lot of irrelevant documents.

   • Formula: $Recall=True\; Positives\; (TP)+False\; Negatives\; (FN)True\; Positives\; (TP)$

   • Interpretation: If you think of the set of all relevant documents as a "target" and the set of all retrieved documents as "shots," then recall is about hitting the target with as many shots as possible. The higher the recall, the more relevant documents are retrieved, but this might mean including some non-relevant documents as well.

2. Precision: Precision is the proportion of retrieved documents that are relevant. It measures how accurate the results are; it tells us how many of the documents returned by the system are relevant. A high precision means that the system returns a lot of relevant documents and very few irrelevant ones.

   • Formula: $Precision=True\; Positives\; (TP)+False\; Positives\; (FP)True\; Positives\; (TP)$

   • Interpretation: Precision is like hitting the target with fewer misses. If you have a high precision, it means that when you retrieve a document, it's likely to be relevant.

Trade-off:

There is often a trade-off

DPO Model

Precision and recall are two important metrics used to evaluate the performance of a binary classification system or algorithm, particularly in contexts such as information retrieval and machine learning.

Recall

Recall refers to the proportion of relevant documents that are correctly identified by the system. It is essentially how many true positives (relevant items correctly identified) are found among all the relevant items in the dataset.

• Formula: $Recall=True\; Positives\; (TP)+False\; Negatives\; (FN)True\; Positives\; (TP)$
• Interpretation: If recall is high, it means the system is good at finding relevant items even if it sometimes returns irrelevant items along with them.
• Goal: Maximize recall means the system tries to find as many relevant items as possible, even if it means some non-relevant items might be included.

Precision

Precision refers to the proportion of relevant documents that are correctly identified by the system. It measures how accurate the relevant items are among those that were retrieved.

• Formula: $Precision=True\; Positives\; (TP)+False\; Positives\; (FP)True\; Positives\; (TP)$
• Interpretation: If precision is high, it means the system is good at distinguishing between relevant and non-relevant items.
• Goal: Maximize precision means the system tries to return only relevant items, possibly missing out on some relevant items that could have been found.

Summary Table

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Prompt 5

Write a polite email asking for an extension on a project deadline.

Base Model

Certainly! Here's a polite email template you can use to request an extension on a project deadline:

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Subject: Request for Extension on Project Deadline

Dear [Manager’s Name],

I hope this message finds you well.

I am writing to kindly request an extension for the completion of our current project. As you may recall, we were initially scheduled to complete the project by [Original Deadline Date]. However, I have encountered some unexpected challenges that have delayed the progress of the project.

To provide more context, the delay is primarily due to [Briefly explain the reason for the delay, e.g., unforeseen technical difficulties, staffing issues, etc.] Despite my best efforts, these obstacles have required additional time to address, which has affected our overall timeline.

I understand that extending the deadline may impact other aspects of our schedule, and I assure you that I am committed to working diligently to ensure that the project is completed as soon as possible.

Could we please discuss this matter further to determine if it is feasible to extend the deadline? If so, I would be happy to propose a new deadline within our existing capacity.

Thank you very much for your understanding and support in this matter. I look forward to discussing this further with you and finding a solution that works for everyone involved.

Best regards,

[Your Name] [Your Position]

[Your Contact Information]

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Feel free to customize this email based on your specific situation and relationship with your manager.

DPO Model

Certainly! Here's a polite email template you can use to request an extension on your project deadline:

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Subject: Request for Extension of Project Deadline

Dear [Supervisor's Name],

I hope this message finds you well.

I am writing to kindly request an extension for the completion of my project due to unforeseen circumstances that have arisen since the original deadline was set. I assure you that I am doing everything possible to meet the revised deadline as soon as feasible.

Here are some details about the situation and the proposed new deadline:

• The current deadline is [Original Date].
• Due to [briefly explain the reason (e.g., illness, unexpected family emergency, technical issues, etc.)], I anticipate that I will be able to complete the project by [Proposed New Date].
• If there are no further delays or complications, I believe I will be able to submit the final version of the project on time.

I understand the importance of adhering to deadlines, and I am committed to ensuring that the quality and integrity of the project remain high throughout the process. I would appreciate your understanding and support in extending the deadline. Please let me know if you require any additional information or documentation to assist with this request.

Thank you very much for considering my request. I look forward to our conversation and working together to ensure the successful completion of of the project.

Best regards,

[Your Name] [Your Position/Department] [Your Contact Information]

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Feel free to adjust it according to your specific situation and comfort level.