LLM Research Blog: The Model That Fits Our Project

During our course, we learned about Natural Language Processing (NLP), GPT-2, TinyGPT, Transformers, and other AI concepts. We also studied computer vision and learned that AI can be used in different ways to solve real problems. These lessons made me more interested in understanding how AI models work and how we can use them to build useful applications.

For my project, I am working on MiniChefGPT, a recipe-generation system. The idea is simple: a user provides the ingredients they have, and the model suggests a meal and explains how to prepare it step by step.

I chose this idea because sometimes people have ingredients at home but do not know what they can cook with them. I want MiniChefGPT to help people discover meal ideas and make better use of the food they already have.

However, choosing a model is not just about choosing the biggest one. I need a model that follows instructions, generates understandable recipes, and can run using the resources available to us. Since I want to experiment without paying for an API, I decided to research three open models: Qwen3-4B-Instruct-2507, SmolLM3-3B, and Phi-4-mini-instruct.

Qwen3-4B-Instruct-2507 was developed by the Qwen team. It has 4 billion parameters and a native context window of 262,144 tokens. It uses the Apache 2.0 licence, which allows people to use and modify the model under the licence conditions.

What interests me about this model is its focus on instruction following, reasoning, and text generation. In MiniChefGPT, the user might say, “I have Cassava, fish, spinach, and tomatoes. Suggest one meal and show me how to cook it.”

The model needs to understand the ingredients and provide a suitable meal with clear steps. It should also follow the requested format instead of giving a long, confusing answer.

Qwen3-4B-Instruct-2507 looks like a strong candidate for this task. However, its full-precision weights need around 8 GB of storage before accounting for the additional memory needed to run it. A free Google Colab T4 GPU has 16 GB of GPU memory, so the model may be possible to run, but memory use must be tested. Quantization can help reduce the memory requirement.

SmolLM3-3B was developed by Hugging Face and has 3 billion parameters. Its standard context length is 65,536 tokens, and its licence is Apache 2.0. It is an instruction-oriented model that supports reasoning and multiple languages.

One reason I considered SmolLM3 is its relatively small size. Since we may have limited computing resources, a smaller model could make experimentation easier.

For MiniChefGPT, I would test whether it can suggest reasonable meals, organize cooking instructions in the correct order, and avoid adding ingredients that the user does not have. I would also check whether its answers are simple enough for different users to understand.

However, a smaller model is not automatically better or faster for every task. Its actual speed and recipe quality need to be measured in the same environment as the other candidates.

Phi-4-mini-instruct was developed by Microsoft. It has 3.8 billion parameters, supports a context window of up to 128,000 tokens, and is released under the MIT licence. It was designed for instruction following, reasoning, and chat-based applications.

I included this model because MiniChefGPT needs more than text generation. It needs to understand a request and respond in an organized way.

For example, if someone provides rice, beans, and vegetables, the model should suggest a suitable meal and explain the preparation steps clearly. It should not ignore the ingredients or produce instructions that are difficult to follow.

Phi-4-mini-instruct is a useful candidate to test because of its instruction-following design and relatively small parameter count. However, I cannot say it is the best model for recipes without testing it on our actual project.

The specifications come from the official model documentation. All three models have public model repositories, so they can be downloaded for local experimentation without relying on a paid text-generation API. Their licences still need to be followed.

The context window is the amount of text a model can process in a sequence. MiniChefGPT normally needs only a short ingredient list and a recipe, so we do not need the largest context window. More important factors are the quality of the recipe, instruction following, speed, and memory use.

After comparing these models, I would choose Qwen3-4B-Instruct-2507 for our first implementation and evaluation of MiniChefGPT.

My main reason is its instruction-following design. Our project needs a model that can take ingredients as input and generate a meal suggestion with clear cooking steps. I believe Qwen3-4B-Instruct-2507 is a strong candidate for this kind of instruction-based task.

Another reason is its Apache 2.0 licence and public model repository. We can download the model and experiment with it locally without paying for each generated response through a hosted API. The public repository does not appear to require accepting a gated model licence, although we should always check the current model card before use.

The main challenge is running it on a free Google Colab T4 GPU with 16 GB of memory. The model's full-precision weights alone need approximately 8 GB, and the runtime also needs memory for other components. I would first try a memory-efficient setup, such as 4-bit quantization, and check whether the model loads and generates recipes successfully.

I would not claim that Qwen is the fastest model because I have not benchmarked all three models on the same hardware. To get evidence, I would test the same ingredient lists with each model, measure the time taken to generate a recipe, and record the GPU memory usage. If Qwen cannot run reliably or produces weaker recipes than another candidate, I would need to reconsider the choice based on those results.

I would test Qwen3-4B-Instruct-2507 using several ingredient lists. For example, I could provide potatoes, eggs, tomatoes, and onions and ask it to suggest one meal with numbered cooking steps.

I would check whether the model uses the available ingredients, follows the requested format, gives steps in a sensible order, and avoids unnecessary explanations. I would also test unusual combinations to see whether it handles them sensibly instead of inventing an unsuitable recipe.

Finally, I would measure its response time and GPU memory usage on Colab. These tests will help me understand whether the model is practical for our project, not just whether its technical specifications look good.

Working on MiniChefGPT helps me connect what we learned in our course with a practical idea. Learning about GPT-2, TinyGPT, and Transformers gave me a foundation for understanding how language models generate text. Now, I want to apply that knowledge to a system that can help people with something they do in everyday life.

My first choice is Qwen3-4B-Instruct-2507 because of its instruction-following capabilities and open licence. However, the final decision should also depend on practical tests, especially recipe quality, generation speed, and whether it runs within the memory limits of a free Colab T4 GPU.

For me, the goal is not simply to use a large language model. It is to understand it, test its limitations, and use it to build something useful. With MiniChefGPT, I want to turn the ingredients people already have into simple meal ideas they can actually prepare.

Note: The model specifications and licences are based on the published model documentation. Speed and GPU memory use still need to be measured in our own Colab tests.