What is LLM Temperature?

When we talk about LLMs (Large Language Models), temperature is a key parameter that controls how “random” the generated text will be.

LLMs predict the next token (word or part of a word) based on a probability distribution over possible tokens.
The temperature setting modifies that distribution:

Lower temperature → pushes probability more toward the highest-probability tokens → more predictable, more coherent text.

Higher temperature → flattens the distribution (or gives more chance to less probable tokens) → more variety, more creativity, but also risks of incoherence.

Why it matters: depending on your use case, you might want precision and consistency (e.g. factual answers, documentation) or you might want creativity (e.g. story-writing, brainstorming)

Configuring Temperature

To control LLM output, temperature isn’t the only factor. Several other parameters also shape the results:

Temperature: Directly controls the randomness of the output. Low values make the model very precise and predictable. Higher values add creativity but reduce stability.

do_sample: If enabled, the model samples from multiple possible tokens instead of always choosing the most likely one. Without this, temperature adjustments won’t matter.

top_k: Limits the model’s choices to the top k most probable tokens. A small value keeps it conservative and reliable; a larger value gives more freedom.

top_p: Instead of a fixed number, the model chooses from the smallest set of tokens whose cumulative probability passes a threshold (e.g., 95%). This keeps variety while avoiding nonsense.

Controlling Output Beyond Temperature

Temperature isn't the only knob. To get output that better fits what you want, you often combine parameters and control mechanisms.


Here are other levers:

❇️Maximum length: how many tokens the model can output. Keeps responses from going off-tangent.

❇️Stop sequences: define sequences that tell the model, “stop here.” Handy for structured output: emails, lists, dialogues.

❇️Frequency penalty: penalizes tokens (words) that are used often in output; discourages repetition.

❇️Presence penalty: penalizes simply for whether a token has already appeared (not how many times). Helps ensure variety.


Combining these with temperature + sampling parameters gives you fine-grained control over what the LLM produces.

Comparing Outputs with IBM Granite

To make this clearer, IBM tested its Granite 3.1 model with a simple prompt:
“Write a story about a data scientist who loves Python.”

At a very low temperature (0.1), the output was extremely safe and predictable. The story was dry, with little detail.
At a medium temperature (0.75), the story became richer. There were more vivid descriptions and a touch of creativity.
At a high temperature (1.25), the text was full of unexpected ideas. It felt more imaginative, but sometimes drifted away from the main topic

When to Use What

To wrap up, here are guidelines for what temperature + settings you might choose depending on your purpose:

For factual, precise work (e.g. reports, summaries, technical writing): use low temperature (0.1-0.4), minimal top_k or top_p, lower randomness.

For creative work (stories, brainstorming, poetry): use higher temperature (0.7-1.2+), allow more sampling, allow higher top_k / top_p.

Always combine with stop sequences, max length, and penalties to avoid repetition or straying.

Experiment: sometimes a moderate temperature + restrictions gives a sweet balance.

What Parameters Are

In AI models, parameters are the numerical values that guide how the system processes information. They’re the internal “settings” that adjust as the model learns, shaping how it interprets data and produces responses. For example, GPT-4 is estimated to have hundreds of billions of parameters working together to predict words and generate text.

Two Key Types of Parameters

Construction Parameters – These define the model’s architecture: how layers of artificial neurons are arranged, connected, and weighted. Think of them as the blueprint or skeleton that gives the model its structure.
Behavior Parameters – These govern how the model acts when given input. They control responsiveness, adaptability, and output style. Behavior parameters influence whether a model gives concise answers, creative dialogue, or factual recall, depending on the data it receives and how it’s connected to external sources.

How Parameters Learn and Why They Matter

When an AI model trains, it doesn’t memorize data — it adjusts its parameters.
Each parameter slightly changes based on how correct or wrong the model’s predictions are. Through millions or even billions of these small adjustments, the model learns complex relationships between words, images, or sounds.

That’s why parameters are often called the “memory” or “knowledge” of a model. The more parameters a model has (and the better it’s trained), the more subtle patterns it can understand — like tone, context, and even emotions in text.

Think of Google Colab like a free online notebook where you can write and run Python code in your browser — no installation needed.

You log in with your Google account, open a new “notebook”, type code into a cell, hit run, and see results immediately.

What makes it really helpful: (1) It gives you access to faster hardware (GPUs/TPUs) for free, so you can experiment with heavier tasks than your
(2) Everything is saved in your Google Drive and you can share the notebook with someone else like you share a Google Doc.

Colab’s machines are temporary

When you open a Colab notebook, Google gives you a virtual machine (VM) that exists only while your session is active. Once you close the tab or stay idle too long, it gets deleted — including everything you installed or downloaded.
But AI engineers bypass that:

They connect their Google Drive and store checkpoints or model weights there (drive.mount('/content/drive')).

They also create setup scripts that reinstall all dependencies automatically each time the notebook starts (!pip install -q torch transformers datasets).
So every time the VM restarts, it rebuilds itself in seconds.

Colab gives real GPUs, but you have to manage memory smartl

yColab’s free GPU isn’t fake — it’s an actual NVIDIA card like Tesla T4, P100, or A100 (Pro + only). You can check which one you got with

:!nvidia-sm


iBut VRAM is limited (12–16 GB). So if your model crashes with “CUDA out of memory,” pros do this

:import torch, g
cgc.collect(
)torch.cuda.empty_cache(


That clears leftover memory.
They also lower the batch size (how many samples are processed at once) or use torch.cuda.amp.autocast() for mixed precision to save VRAM while training large models