Customizing Small Language Models: LoRA vs. Full Fine-Tuning

I see business owners, small teams and decision makers who want to integrate AI better into their work and day to day operations. Not surprisingly, there is some confusion about what solution is best, especially if you’ve made the decision to use a locally hosted, private AI model. So, I would like to show some examples of what two kinds of fine-tuning can do.

If you want to use a language model for any kind of specialized purpose, where niche or special domain knowledge may be involved, you have the option of relying on vector databases and document searches (RAG), fine-tuning a model, or a combination of both. The first solution is basically the AI looking through the available knowledge, pulling bits and pieces, and using those pieces as supplemental context to the answer it is going to generate. Fine-tuning however, involves actually teaching the model itself some new things. Hopefully this post can shed some light on what the best option would be for your use case.

Let me first say, that the most important aspect of fine-tuning is the data you use to train on. You have to be very deliberate about how you construct and build your data set to get the best results. Garbage in, garbage out, they say, so make sure the data represents actual use cases, is properly cleaned, and all that good stuff before you start any kind of fine-tuning.

Assuming that you have the data you need, the next big question is: what kind of fine-tuning are you going to need? And here, there are two main approaches: low-rank (LoRA) and full fine-tuning.

LoRA is great for gentle steering, emphasis on specific aspects of existing knowledge, and tone, but it does not pick up a lot of new knowledge. Full fine-tuning on the other hand, lets you add new, specific knowledge directly into the AI model because you are training the entire model. Why not just go for a full fine-tuning every time then? Because it comes with the risk of causing the model to collapse and forget existing training, including how to do things like use tools correctly to search documents. Full fine-tuning also requires more computing power (VRAM) to pull off depending on the size of the base model you are working with.

With either approach, there may be some trial and error involved as well, depending greatly on the type and amount of data, output requirements, model architecture and more, but that lies beyond the scope of this post.

The setup

For this experiment, I want to show the difference in output between a basic model and the two kinds of fine-tuning mentioned above. I am using a data set focused on science-fiction and fantasy. This data set was created using a number of wikipedia-pages as source for question and answer pairs to be generated and is, like everything else in this post, available to download for free via my Huggingface profile.

I chose a Llama 3.2, 1-billion parameter model as the starting point, and trained it twice on the same data set for exactly 1 epoch; meaning that the model saw each entry exactly once. First I used Low-Rank (LoRA) fine-tuning where only a small part of the network is tuned. For the second version, I did a full fine-tuning where every single parameter in the model was being adjusted.

Download the LoRA fine-tuned model directly or search for theprint/Llama3.2-1B-FantasySciFi in LM Studio.
Download the Fully fine-tuned model as well, or search for theprint/Llama3.2-1B-FantasySciFi-Full in LM Studio

The only difference between the two fine-tuning sessions was the learning rate (LR), which represents how much knowledge is absorbed per training step. As is common practice, the learning rate was a lot higher on the LoRA fine-tuning (2e-4) than on the full one (2e-5). The lower LR is to reduce the chance of catastrophic forgetting during the full fine-tuning.

Test Questions

The models were tested without any system prompt at all. I wrote out 6 test questions and each one was served to the model being tested without any additional context. For this post, I will share the 3 most interesting ones, in terms of the difference between the three models tested. The questions featured are:

What is Terry Pratchett most famous for?
What are 3 novels, I should read this summer?
Give me an example of a well-written character arc.

Note that of these 3 questions, only the first one directly references genre-specific data, namely the author Terry Pratchett. The others were deliberately left open to see if the model would drift towards answers rooted in the training data. Below are the answers given by all 3 models, along with a few thoughts on the differences that stood out to me.

Tricky Terry

On the question “What is Terry Pratchett most famous for?” none of the models gave a perfect answer, which makes it a perfect example to show. All 3 answers have factual errors related to the works of mr. Pratchett, which is important because: no matter which approach you choose, AI can still fail. It may fail a bit closer to target, but if your field is legal or medicine for example, “close enough” may not cut it. Proceed with caution!

Basic Llama 3.2 1B Instruct model’s response

“Terry Pratchett was a British author best known for his fantasy series “”Discworld””, which combines humor, satire, and social commentary. He is particularly famous for creating:

The Discworld Series: A collection of nine novels that are set on the flat disc balanced on the backs of four giant turtles, who in turn are floating through space.
Characterization of satire and absurdity: Pratchett’s work often uses humor to critique contemporary issues, such as politics, society, culture, and morality, with a playful twist.
Memorable characters: The Discworld series features numerous iconic characters, including:
- Rincewind (the inept wizard)
- Death (the personification of death who travels at high speed)
- Granny Weatherwax (a witch who is also a strong and independent woman)
- A’Tuin (the god of the sun)
Wordplay and linguistic humor: Pratchett was known for his clever use of language, puns, and wordplay, which added to the comedic effect of his works.
Satire of authority and bureaucracy: Many of his novels poke fun at government, institutions, and social norms, often using absurd situations and characters as a way to comment on the human condition.

Some notable books in the Discworld series that are often cited as Pratchett’s most famous works include:

“”The Colour of Magic”” (1983)
“”The Light Fantastic”” (1986)
“”Mort”” (1987)
“”Small Gods”” (1992)
“”Wyrd Sisters”” (1988)

Terry Pratchett passed away in 2015, but his Discworld series remains a beloved and iconic part of fantasy literature.”

LoRA Fine-Tuned model’s response

Terry Pratchett was a British author best known for his Discworld series of fantasy novels, which follows the misadventures of an ensemble cast of characters in the fictional world of Ankh-Morpork. His works are renowned for their wit, humor, and satire.

Some of his most notable contributions to literature include:

*   **Discworld**: Pratchett’s Discworld series has become a beloved part of popular culture.
*   The series explores themes such as politics, power struggles, social commentary, and human nature through the lens of comedic fantasy.
*   Works like “The Colour of Magic” (1971) and “Guards! Guards!” (1989) showcase Pratchett’s unique writing style and ability to balance humor with deeper exploration.

Full Fine-tuned model’s response

“Terry Pratchett was a British author best known for his Discworld series, which consists of fantasy novels that parody and satirize various aspects of life. His works often feature:

Discworld: A flat, disc-shaped world created by the wizard Rincewind, which serves as the main setting for many of Pratchett’s stories.
Parodying mythology: Pratchett frequently draws inspiration from mythology and folklore to create humorous, irreverent takes on classic mythological themes.

Some notable examples include:

The Colour of Magic
The Light Fantastic
Equal Rites

Pratchett’s Discworld series has been widely acclaimed for its unique blend of humor, satire, and imagination. He was known for his witty writing style and ability to create memorable characters like Rincewind and Death.

While Pratchett also wrote other works, such as Mort by Stephen Baxter (a novella set in the same universe), it’s his Discworld series that has made him most famous among fans of fantasy literature.”

Summer Read Pivot

When asked “What are 3 novels, I should read this summer?” the standard instruct model mentions “The Power” as the most relevant suggestion, setting a great point of comparison to the two fine-tunes which (spoiler) both deliver better responses in this category. I think this shows that without any kind of fine-tuning, it all comes back to the prompt. The LoRA suggested the book “The Night Circus“, which is a bit better tied to the data set, contextually. Meanwhile the full fine-tune suggests both of those and “The City & The City” which is arguably more of a deep-dive title, relevant to the data set.

Basic Llama 3.2 1B Instruct model’s response

“3 recommendations for novels to enjoy this summer.

“”The Seven Husbands of Evelyn Hugo”” by Taylor Jenkins Reid: A captivating and heartwarming story about a reclusive Hollywood star who recounts her seven marriages to a young journalist.
“”The Nightingale”” by Kristin Hannah: An emotional and suspenseful novel set in France during World War II, exploring the lives of two sisters as they struggle with the war, sacrifice, and survival.
“”The Power”” by Naomi Alderman: A thought-provoking speculative fiction novel that explores a world where women suddenly develop the ability to release electrical jolts from their fingertips, allowing them to dominate and control society.

All three novels offer engaging stories with relatable characters, making them perfect for a summer read.”

LoRA Fine-Tuned model’s response

Here are three novel recommendations:

1. “The Night Circus” by Erin Morgenstern (2011)
   A magical tale about a competition between two young magicians who are trained from childhood to compete against each other.

2. “The Last Romantics” by Tara Conklin
   A sweeping family saga that explores themes of love, loss, and legacy through the eyes of four siblings growing up in the 1970s.

3. “The Song of Achilles” by Madeline Miller (2011)
   A beautifully written novel that reimagines Homer’s Iliad from the perspective of Achilles’ lover Patroclus, exploring themes of love, war, and identity.

Full Fine-Tuned model’s response

“Here are three highly-recommended novels across various genres that you might enjoy:

The Night Circus by Erin Morgenstern:
A magical and imaginative tale about a competition between two young magicians who become enchanted by each other.
The Power by Naomi Alderman:
A thought-provoking novel that explores what would happen if women suddenly developed the ability to release electrical jolts from their fingertips, allowing them to dominate and control society.
The City & The City by China Miéville:
A police procedural set in a world where two cities coexist in the same space, and citizens are trained to “”unsee”” the other city. This novel explores themes of perception, reality, and power dynamics.

These novels offer unique perspectives on human nature, society, and individual agency.”

The Force is Strong in This One

When “Give me an example of a well-written character arc” is requested, both the basic instruct and the LoRA fine-tuned models invented new characters, embarking on a creative writing exercise. But in the fully fine-tuned model we see something completely different: instead of inventing a character, the model uses the example of Luke Skywalker to describe what a character arc is. This is significant because we see a change, not just in the output generated but in the behavior of the model itself.

Basic Llama 3.2 1B Instruct model’s response

“Here is an example of a well-written character arc:

Original Character:
Meet Alex, a young professional in her late 20s who has just moved to the city for a fresh start after graduating from college. She’s determined, ambitious, and always striving to prove herself.

Inciting Incident:
Alex lands a high-paying job at a prestigious marketing firm, but soon discovers that she’s working long hours with little creativity or autonomy. Feeling undervalued and unfulfilled, she starts to question her decision and the true nature of her work.

Turning Point:
While working late one night, Alex has a heart-to-heart conversation with an old friend, Rachel, who was also a college classmate. Rachel shares her struggles as a single mother trying to make ends meet while pursuing higher education. This realization sparks a deep sense of empathy and compassion in Alex, making her question whether she’s truly happy with her current career path.

Setback:
Alex tries to shake off the doubts by taking on more responsibilities at work and trying to prove herself to her boss. However, this newfound focus leads to burnout and exhaustion, causing her to withdraw from social life and relationships with friends and family.

Midpoint:
As Alex’s depression and anxiety grow, she reaches out for help from a therapist who helps her explore the underlying causes of her feelings. Through therapy, she begins to understand that her true passion lies in photography, not marketing. She starts taking photos in her free time, which brings her a sense of joy and fulfillment.

Climax:
As Alex’s confidence grows, she decides to take a leap of faith and leave her job to pursue photography full-time. This decision is met with both excitement and fear from her friends and family, who worry about her financial stability and social isolation.

Resolution:
Alex struggles to balance her passion for photography with the practicalities of making it a career. She faces setbacks and rejections, but through hard work and determination, she finally lands her first client and starts building a successful business.

Character Growth:

Alex has become more empathetic and compassionate towards others, particularly those in need or struggling to make ends meet.
She’s developed greater self-awareness and emotional intelligence, learning to recognize and manage her own feelings and emotions.
Her passion for photography has transformed from a job into a true calling, reflecting her authentic self and values.

This character arc illustrates several key elements:

Internal conflict: Alex faces an internal struggle between pursuing a career that doesn’t fulfill her or finding happiness and fulfillment through another path.
Escalating stakes: As Alex’s doubts grow, she faces increasing pressure to prove herself and find success in her chosen career.
Transformation: Through the turning point conversation with Rachel, Alex experiences a significant shift in perspective, leading to greater self-awareness and confidence.
Rite of passage: Alex’s decision to leave her job and pursue photography marks a major milestone in her life, symbolizing growth and self-discovery.

Overall, this character arc demonstrates how a well-written story can create a compelling and memorable character journey, showcasing the transformative power of empathy, self-awareness, and determination.”

LoRA Fine-Tuned model’s response

“Here is an example:

Character Arc: From Naive to Confident

Initial State: Emily, a young and idealistic journalist, arrives in Tokyo with a clear vision for her story. She’s eager to make a difference and prove herself as a capable reporter.

Inciting Incident: During a meeting at the newspaper office, she meets her editor, who challenges her on the ethics of reporting sensitive information. This encounter sparks a moment of doubt and uncertainty within Emily.

Rising Action: As Emily delves deeper into the story, she realizes that her initial assumptions were incorrect. She begins to see the complexities of human nature and the blurred lines between right and wrong. Her editor’s feedback serves as a catalyst for this growth, pushing Emily out of her comfort zone.

Climax: During an interview with a source who provides valuable insights into the story, Emily experiences a moment of profound vulnerability. She begins to confront her own biases and limitations, allowing her to grow as a journalist and as a person.

Falling Action: As Emily works on the article, she faces resistance from colleagues who question her methods or dismiss her concerns. This external pressure forces Emily to reevaluate her approach and consider alternative perspectives.

Resolution: Through this process of self-discovery, Emily develops a more nuanced understanding of journalism ethics and human nature. Her work is met with praise, but also criticism from some quarters. This mixed response serves as a testament to the complexities of storytelling and the importance of considering multiple viewpoints.”

Full Fine-Tuned model’s response

“One classic example is the transformation of Luke Skywalker from a young farm boy to a confident Jedi Knight.

Initially, we see Luke as a naive and impressionable individual who is drawn into the world of Star Wars through his friendship with Han Solo. However, as he trains with Yoda on Dagobah and learns about the Force, we witness Luke’s growth from an uncertain teenager to a heroic young adult.

One key aspect of Luke’s character arc is his development as a leader. Initially, he struggles to assert himself in battles against the Empire, but through his experiences, he gains confidence and becomes more effective at guiding others.”

March 24, 2026March 24, 2026

Introducing: LM Data Tools

LM Data Tools is a suite of tools for synthetic data generation that I’ve been developing since I started working with fine-tuning language models back in 2023. What started as a handful of independent python scripts now share a web interface (and API) that makes them easy to work with. In this post, I will go over what the different tools are, and give an example of how to use one of them.

Fine-tuning language models is most definitely a rabbit hole, but if you’re brand new to the concept, we can summarize it as a way to specialize a pre-trained AI model in some way. There are different approaches and techniques, and twice as many opinions about the best ones to use depending on the desired outcome.

If you’re not already a data scientist or engineer, it can seem overwhelming before you even begin. On that note, a huge shout-out to the Unsloth crew, whose colab notebooks were my introduction to getting hands-on with fine-tuning. Without them, learning this stuff would have taken much longer. The notebooks gave me a great starting point, and as I learned more about each aspect and parameter involved in the process, I could modify and tweak the notebooks to match.

Before I get too deep into the details, the reason for even building a tool set like this in the first place, was that I needed an straightforward way to get topic-specific data without wasting a bunch of time looking through public data sets, hoping to find exactly what I needed. I was learning about fine-tuning LLMs and data synthesis as part of that, and for me, the best way to learn something is to build it. In this case “it” was data.

Early Version

Despite this being a collection of tools, some of which have been around for a long time (by AI standards at least), bringing them together was not as easy as it may sound, and there are definitely still bugs to iron out but even so, I am very happy with it and use it enough, that I thought it was time to share LM Data Tools with you.

Tools in the Box

LM Data Tools consists of 6 core tools and 2 strict utilities. The core tools all involve generating new data, whereas the utilities only modify existing data sets. All the features are also FastAPI integrated, in case you want to incorporate any of them into other workflows. Without further ado, let me go over the tools included.

Tool	Description
DataBird	Give it a list of topics and optionally a list of user perspectives, and this tool will generate a number of questions and answers on those topics, from said perspectives. Can generate the perspectives as well.
DataPersona	This tool takes a list of prompts (existing data) and applies a persona to responses. It can write 1 or 2 replies per query, and you can have both or let the built-in evaluation feature pick the best one. The personas from this tool can be imbued into responses in other tools as well. You can add and edit personas directly via the web interface.
DataQA	This is a RAG tool. You feed it a list of URLs which are then scraped, and as with DataBird, you can provide specific user perspectives. A number of question and answer pairs are then generated from those perspectives, and based on the sources provided.
DataWriter	This tool was specifically made for pre-training purposes and will generate any number of made-up text documents, from blog posts to meeting summaries. The document mix is based off of a weighted list of topics.
DataConvo	Sometimes you want multi-round conversations to train on. This tool can take any single-round conversation data set and expand it into longer entries.
DataThink	This tool can add <think></think> blocks to existing data or generate data which already include reasoning blocks. If a persona is chosen, it will only be applied to the response part, not the reasoning.
DataMix	This utility lets you mix and match data sets from Huggingface to make a new, custom data mix.
Reformat	Need to convert from alpaca-format to ShareGPT, or the other way around? This utility reformats existing data sets without changing any of the prompts or responses.

Any Provider via OpenAI API

The tool set uses the OpenAI API to communicate with LLM servers, and you can connect to any provider that supports this API. If you’re running a local AI server, you can simply type in the server IP and use that. Your experience and the quality of the generated data will depend on your model of choice, of course.

I use the tools with LM Studio for locally hosted models and typically fall back on OpenRouter for API usage. OpenRouter often have “stealth models” that can be used for free, and for something like this, free compute is always welcome.

Example: Text Analysis

Let’s imagine that we want to fine-tune a model to help with text analysis. For this, we will use DataBird with the “Curious Analyst” persona.

Next, we are going to need some topics to base the data set around. You will get the best overall results if you base this list on topics that are already adjacent. For this example, we are going with “finding the meaning and intention behind prose“, “storytelling and plotting“, and “learning from text analysis“.

Let’s also add the following perspectives manually: “an English major struggling with text analysis“, “an author who needs help with their next book“, and “a technical writer who wants to start writing fiction“.

It’s as easy as that. The more topics and perspectives you add, the larger the data set you’ll end up with. After generation and quality evaluation, we are left with a fresh, small data set of 77 entries that can be used for fine-tuning any language model.

The example data set can be downloaded from Huggingface and the whole LM Data Tools suite is open source on github.

March 12, 2026March 12, 2026

Tweaking a Chat Model with Direct Preference Optimization (DPO)

Recently, I completed my first real DPO fine-tuning of a model with encouraging results, and I wanted to share the process with you.

If you have no idea what DPO is, here’s a simplified description: Direct Preference Optimization is a form of fine-tuning applied to large language models where the AI learns to align with specific, pre-defined criteria. The data itself consists of a query and not one, but two responses. Each entry has a “chosen” and a “rejected” answer, and the idea is to teach the model the difference. Besides the data, specific criteria are given (or preferences, if you will) and the model learns to favor responses matching those criteria. With enough training, the model eventually adheres to these preferences outside of training as well.

If you don’t care and just want to go straight to the download section, just scroll to the end.

Why Apply DPO to a General Chat Model

I had already applied the more basic Supervised Fine Tuning (SFT) to a small 3-billion parameter language model, not to make it an expert in anything technical, but to make it a good general chat bot. I define “good” in this context, as defaulting to an informal, conversational tone without lots of fancy formatting, a model that values empathy without falling into the sycophancy trap.

The GeneralChat GPT data set was created specifically for the SFT part of the training and it did an OK job at nudging the underlying Llama 3.2-model in the right direction, but I wasn’t really happy. However, since I’d already wanted to try out DPO, it became an obvious candidate.

The DPO Data Set

I had a second data set on hand, also geared towards the kind of conversational tone I wanted, but as an alpaca-formatted data set, it only contained 1 response per query. This would work fine as the “chosen” reply in my training, but I needed a “rejected” counter point for the model to compare to. The solution was simple: Have another, much smaller model write responses to all the same entries and classify them as the rejected ones.

I used a Llama 3.2 1B Instruct model to write all the bad answers, then reformatted the data to contain both responses and the DPO data set was done. I did do a quick look through these “rejected” answers to make sure they weren’t completely off base, so there was nuance for the model to actually learn. Otherwise, I could have just put “I like cheese.” as the value for all the rejected answers (although I do love cheese).

Eval Criteria and Calibration

Creating the data set is only half the battle in the case of DPO-training, however. I needed samples that had additional information embedded, which could be used as evaluation criteria during the actual training. In addition to the chosen and rejected responses, the evaluation data also scores that show how well the chosen response embodies the criteria, and a written reason why this answer is the winning one.

It is important to note that for DPO training, the exact criteria are specific to the result you are looking for. As in, you can’t just say “make it good!” and apply this to every DPO-training, expecting to get any value out of it. For my general chat model, these were the specific criteria:

Is the response given in a naturally flowing, conversational and empathic style?
Does the response address the query without sycophancy or undeserved flattery?
Does the response have a hint of character and personality?
Anti: Is the response highly structured and formatted

The first 3 are positive and scored from 1-7, while the last one is a negative score from 0-2, given that I specifically wanted less formatting and structure in these responses.

This is where the real work begins! I needed to create the calibration set, a small subset of entries that contain all that evaluation data. Together, these entries will become the preferences that guide the training. So I built a quick web interface for data entry and manually scored and described 50 calibration entries.

This calibration set is the secret sauce and the most important part of the entire process. If done well, it will guide the DPO training exactly where you want it to go, but if it’s too ambiguous or inconsistent, you’ll likely end up with a degraded model after DPO-training is done.

Evaluation Sample

Here is a data sample from the evaluation set. Expand to see a full json-entry with scores and reason provided as guidance during training.

{
    "chosen": "That’s a reasonable starting point. The sock anecdote relies on a bit too much forced whimsy. Let's focus on something more grounded. \n\nThe problem is, you're trying to *manufacture* interest. Networking isn’t about delivering a pre-packaged story; it’s about establishing a connection. Instead of framing your process as quirky, describe the underlying logic. \n\nHere’s what I’d suggest: “I tend to approach problems systematically, but often find that the most useful insights emerge from seemingly unrelated areas. For instance, I was working on [mention specific project] and realized a technique used in logistics – optimizing for minimal movement—could be applied to [relevant aspect of your work]. It highlights how diverse fields can offer surprising solutions. If you're interested in understanding *how* I connect those dots, we could talk about the project.”\n\nIt’s direct. It establishes that you have a process and demonstrates it through a concrete example. Then, offering to discuss further gives them an opening to engage if they find it remotely interesting. Don’t try to ‘wow’ them; demonstrate a clear way of thinking.",
    "confidence": "clear",
    "criteria": [
        {
            "score": 6,
            "text": "Is the response given in a naturally flowing, conversational and empathic style?",
            "type": "primary"
        },
        {
            "score": 7,
            "text": "Does the response address the query without sycophancy or undeserved flattery?",
            "type": "primary"
        },
        {
            "score": 5,
            "text": "Does the response have a hint of character and personality?",
            "type": "secondary"
        },
        {
            "score": 0,
            "text": "Is the response highly structured and formatted?",
            "type": "anti"
        }
    ],
    "criteriaScores": {
        "0": 6,
        "1": 7,
        "2": 5,
        "3": 0
    },
    "evaluator": "theprint",
    "pairIdx": 0,
    "prompt": "Help me come up with a witty anecdote about my 'unique' creative process to break the ice with someone at this networking event and get them interested in my work without having to resort to small talk.",
    "reasoning": "The response is helpful by answering the query but also pushing back against the notion of manufacturing anecdotes to start conversations.",
    "rejected": "I'd be happy to help you craft a witty anecdote that showcases your creativity and sets the stage for conversation. Here are a few options:\n\n1.\n\"I was once asked to illustrate a children's book on 'how to spot a stranger who isn't listening.' Sounds easy, right? Well, the twist is that my main character has no idea what a stranger sounds like... or they're not even there.\"\n2.\n\"One day I realized I'd forgotten how to breathe while working. After re-reading my notes 20 times, I finally got up and took a deep breath – which led me to a brilliant idea for a new project that combines writing and origami.\"\n\n3.\n\"I spent an entire weekend in my garage as an 'artistic experiment,' where I created... 17 identical paintings of my own backside. The results? A 5% improvement in my self-esteem, and a much more interesting blog post.\"\n4.\n\"As I was brainstorming ideas for a new project, I accidentally superglued a paintbrush to my hand. Now that's what I call 'drawing inspiration'\"\n\n5.\n\"I once tried to write a story about time travel, but kept getting sidetracked by a sudden urge to dance the Macarena on top of a soapbox in Times Square.\"\n\nFeel free to pick the one that tickles you (pun intended), or use them as inspiration to craft your own unique anecdote. The goal is to share something unexpected and memorable, which will likely spark interest and curiosity in others.\n\nWhich one do you like best? Or would you like me to come up with some more options for you?",
    "selected": "a",
    "session": "tom-dpo-1003",
    "timestamp": "2026-03-10T14:54:19.397Z"
}

Example Outputs

Below are two examples that show the output from the GeneralChat model before and after the DPO fine-tuning. I have included a few notes for each one.

“Do you think I could learn how to master networking at events even though I am an introvert by talking to myself in the mirror?”

No DPO-training

With DPO-training

You can definitely practice your event networking skills in front of a mirror. Talking yourself through scenarios or practicing introductions with yourself is totally doable and might be just what you need to build confidence before interacting with actual people at events.

Try breaking it down into smaller steps: think about how someone else would approach the situation, then visualize yourself doing the same thing – confidently and smoothly. It’s a great way to prepare mentally for those moments when you’ll actually have conversations in person.

You can definitely use self-talk as a tool for practice—talking through scenarios in front of that mirror really helps build confidence. Just remember, it’s one thing to rehearse conversations with yourself; another is actually engaging others at events.

Start small: maybe try striking up brief chats about everyday topics like weather or hobbies, and see how you feel afterward. Keep things light initially so they don’t become overwhelming for either of you—just observe the interaction without needing an immediate response from them. Over time, that practice will translate into smoother networking experiences when it counts most.

The difference in the example above is the gentle pushback introduced in the DPO-trained model, basically telling the user not to expect miracles by reminding them that live events are going to be different from practicing at home. The other model is uncritically encouraging.

“I worry that my poor cooking skills will impact me socially, but I don’t have time to take a cooking class, so what should I do?”

No DPO-training

With DPO-training

Firstly, consider your current social interactions—whether they’re one-on-one conversations with friends or larger gatherings such as potlucks and parties. If you find yourself frequently feeling anxious about hosting these events due to the pressure of cooking meals, perhaps it would be beneficial to start small by preparing simple dishes like salads or sandwiches that require minimal preparation time.

Another option could involve enlisting help from others who might appreciate assisting with meal planning and execution—this approach can not only alleviate your stress levels but also foster a sense of community among those involved in these gatherings.

Remember, social interactions are about more than just the food you serve; they’re an opportunity to connect and build connections over shared experiences rather than striving for perfection.

Lastly, if cooking seems like something that’s becoming increasingly daunting due to time constraints or other factors impacting your ability to prepare meals effectively without feeling overwhelmed by its demands on both physical effort as well as mental focus, consider alternative options such as ordering takeout from reputable establishments known for their quality dishes and presentation styles. This approach can help maintain a level of comfort in social settings while minimizing stress related to meal preparation.

Ultimately, it’s essential that you find ways to navigate these situations without compromising your emotional well-being or feeling inadequate about being unable to meet certain expectations set forth by others when engaging in such activities as cooking meals for gatherings and events.

Cooking can feel like another barrier when you’re already busy—especially if it’s something that might make socializing harder for you. It’s easy to get caught in the idea of needing a perfect dish or a fancy recipe, but honestly, most people are more interested in how you’re doing than what’s on their plate.

Instead of looking at cooking as another skill to master, try thinking about it like this: can I make something simple that feels good? Maybe even just heating up leftovers and adding a bit of seasoning—just enough to get the taste right. The goal isn’t perfection; it’s showing up with effort.

If you want more than just reheating, maybe consider one meal per week where cooking is part of your routine. Even if it takes 15 minutes instead of an hour, that small step can make a difference in how comfortable you feel when sharing food.
You don’t need a class to start feeling better—just being present and showing care through what little time or effort goes into preparing something for yourself or others.

In this example, the non-DPO model provides a long response that tries to tackle the cooking-problem directly, providing a way for the user to end up with food for their guests. The DPO-trained model provides a shorter answer that goes more to the root of the issue, which is the social anxiety, not the cooking itself, while also addressing that part. Based on my entirely subjective opinion, the shorter response holds more value in this context.

Download GeneralChat-Llama3.2-3B-DPO

Want to try the model out for yourself? It is completely free to download from Huggingface and comes in two versions:

1. 16-bit full weight model

2. GGUF quantization

If you’re not sure how to use a locally hosted AI model, take a look at LM Studio or Ollama.

I’d love to know what you think, if you decide to try the model out. Follow me on Huggingface to see all my models and data sets as soon as they are published.