Sustainable Swap Suggester

How Sustainable Swap Suggester works

Press Load model once: your browser downloads Gemma 3 1B instruction-tuned (web build) (728 MB) from this site and keeps it in its cache. Then add your text and press Analyze locally. The model runs on this device using your graphics card, so nothing you enter is sent to a server. Until the model is loaded, or if your device cannot run it, the tool falls back to a simple keyword check and labels the result as the fallback.

Gemma 3 1B instruction-tuned (web build) is a small open language model from Google. The tool sends it the instruction below plus your text, asks for a JSON answer with fixed fields, and shows each field as a card. It runs at temperature 0, so the same input gives the same answer on the same device, though different graphics cards can word it slightly differently.

Inputs longer than 6,000 characters are cut to that length before they reach the model, and the result says so. The model only sees what you paste: it does not look anything up.

The instruction also carries one worked example: the built-in sample with the answer the larger Gemma 4 E2B model gave for it. That example is what lets this smaller model follow the answer format reliably.

The instruction the model gets

You suggest practical, lower-carbon or lower-waste swaps for everyday habits the user lists. Give one realistic swap per habit, ordered from easiest to hardest. Do not quote carbon, money or percentage savings, and do not recommend specific brands, products or stores.

What it returns

• Swaps: One entry per habit, written as 'habit → swap'.
• Easiest place to start: The single swap that needs the least effort or cost, and why.
• Bigger changes: Swaps that need more money, time or planning, from the list above.
• Already low-impact: Habits in the text that are already a good choice, if any.

Worked example

The model's output for the built-in sample, recorded on a desktop browser. Your run may word things slightly differently.

Current habit: disposable coffee cups most weekdays, plastic produce bags, beef dinners twice weekly, and old incandescent bulbs in hallway fixtures.

Swaps

disposable coffee cups → reusable coffee cup plastic produce bags → reusable shopping bag beef dinners twice weekly → plant-based protein options old incandescent bulbs in hallway fixtures → LED bulbs

Easiest place to start

reusable coffee cup → because it’s the easiest way to avoid single-use containers.

Bigger changes

beef dinners twice weekly → plant-based protein options old incandescent bulbs in hallway fixtures → LED bulbs

Already low-impact

None found

The model

The files are served from this site, versioned so a cached copy is never stale. Nothing is fetched from a third party while you use the tool.

You can switch models in the tool. Also available: Gemma 4 E2B instruction-tuned (web build) (2.0 GB). The larger language model gives more thorough answers but is a bigger download and needs a stronger device.

Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.

Limitations

• Small language models make mistakes: they can miss an item, merge two, or state something more confidently than the text supports. Check the result against your input before you rely on it.
• It needs WebGPU and about 4 GB of memory, so it is offered on desktops and laptops, not on phones and tablets. Devices that cannot run it get the keyword fallback, clearly labelled.
• Results describe the input you gave, not the real-world situation behind it. Review them yourself before acting.

Good uses

• Triage climate and sustainability notes before deeper review
• Prepare consistent labels for repeat workflows
• Reduce manual sorting on small batches
• Keep sensitive drafts in the browser preview