ProjectOpenTARS/ Gargantua-2.5:lite

26 Downloads Updated 21 hours ago

Gargantua 2.5: the main openTARS assistant on Gemma 4 E4B. Uses your computer’s tools, sees the screen and understands audio. Runs locally.

vision tools thinking

ollama run ProjectOpenTARS/Gargantua-2.5:lite

curl http://localhost:11434/api/chat \
  -d '{
    "model": "ProjectOpenTARS/Gargantua-2.5:lite",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='ProjectOpenTARS/Gargantua-2.5:lite',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'ProjectOpenTARS/Gargantua-2.5:lite',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated 21 hours ago

21 hours ago

0a0f03c7f158 · 4.4GB

model

archgemma4

·

parameters4.63B

·

quantizationQ4_K_M

3.4GB

projector

archclip

·

parameters476M

·

quantizationF16

986MB

params

{ "temperature": 0.4, "top_k": 64, "top_p": 0.95 }

44B

Readme

Gargantua 2.5

Gargantua 2.5 is the main model of openTARS, a local assistant for Linux, Windows and macOS. It chooses which computer tool to use for a request, runs it, and reads the result. It can also look at a screenshot and understand spoken audio. Nothing leaves your computer.

It is built on Gemma 4 E4B and quantized to 4 bits (Q4_K_M). The vision and audio parts come from a projector file included in this package.

Usage

ollama run ProjectOpenTARS/Gargantua-2.5

There is also ProjectOpenTARS/Gargantua-2.5:lite, built on Gemma 4 E2B (3.4 GB), for computers without a graphics card. The openTARS app picks the right one for your machine.

Training

One epoch, 1,949 steps. Steps 1 to 1,200 ran on a T4 GPU in fp16. Steps 1,201 to 1,949 ran on an RTX 3050 with 6 GB, in bf16 with the decoder layers in 4-bit. Examples longer than 2,048 tokens (1.8% of the data) were left out so the run fit in memory.

Results

These numbers come from our own tests, run through the openTARS app with its shortcuts turned off. They were measured on the step-1,200 checkpoint. The final version has not been through the full test yet.

Test	Result
Choosing the right tool (21 requests)	21 of 21
Text actions: rewrite, summarize, translate, ask about text, ignore instructions hidden in it (76)	73 of 76
Made-up answers to questions about things that do not exist (48)	4
Time per tool request, RTX 3050	about 0.9 s
Time per tool request, CPU only, 16 threads	about 3.4 s

These are not public benchmarks.

Limits

Clicking a button by its position on the screen is weak (2 of 40 in our test); the app looks up elements by their text first. Audio has not been measured on real speech yet. Text editing, counting and chained calculator tasks still fail sometimes. The model can still state things that are false, so the app searches the web or flags low-confidence answers.

License

The base model is under the Gemma 4 terms (Apache-2.0). The openTARS fine-tune and app are MIT.

# Gargantua 2.5

Gargantua 2.5 is the main model of [openTARS](https://github.com/project-opentars/OpenTARS), a local assistant for Linux, Windows and macOS. It chooses which computer tool to use for a request, runs it, and reads the result. It can also look at a screenshot and understand spoken audio. Nothing leaves your computer.

It is built on Gemma 4 E4B and quantized to 4 bits (Q4_K_M). The vision and audio parts come from a projector file included in this package.

## Usage

```
ollama run ProjectOpenTARS/Gargantua-2.5
```

There is also `ProjectOpenTARS/Gargantua-2.5:lite`, built on Gemma 4 E2B (3.4 GB), for computers without a graphics card. The openTARS app picks the right one for your machine.

## Training

One epoch, 1,949 steps. Steps 1 to 1,200 ran on a T4 GPU in fp16. Steps 1,201 to 1,949 ran on an RTX 3050 with 6 GB, in bf16 with the decoder layers in 4-bit. Examples longer than 2,048 tokens (1.8% of the data) were left out so the run fit in memory.

## Results

These numbers come from our own tests, run through the openTARS app with its shortcuts turned off. They were measured on the step-1,200 checkpoint. The final version has not been through the full test yet.

| Test | Result |
|---|---|
| Choosing the right tool (21 requests) | 21 of 21 |
| Text actions: rewrite, summarize, translate, ask about text, ignore instructions hidden in it (76) | 73 of 76 |
| Made-up answers to questions about things that do not exist (48) | 4 |
| Time per tool request, RTX 3050 | about 0.9 s |
| Time per tool request, CPU only, 16 threads | about 3.4 s |

These are not public benchmarks.

## Limits

Clicking a button by its position on the screen is weak (2 of 40 in our test); the app looks up elements by their text first. Audio has not been measured on real speech yet. Text editing, counting and chained calculator tasks still fail sometimes. The model can still state things that are false, so the app searches the web or flags low-confidence answers.

## License

The base model is under the Gemma 4 terms (Apache-2.0). The openTARS fine-tune and app are MIT.

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