Downloads · 30 days
304
14% of all-time downloads
prithivMLmods/Gliese-CUA-Tool-Call-8B-Localization-GGUF
Gliese-CUA-Tool-Call-8B-Localization-GGUF is a image-text-to-text model from prithivMLmods. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
Gliese-CUA-Tool-Call-8B is a Computer Use Agent (CUA) multimodal model based on Qwen2.5-VL-7B-Instruct, designed for GUI understanding, UI localization, and action execution across web, desktop, and mobile environment…
Downloads · 30 days
304
14% of all-time downloads
All-time downloads
2.1K
Public
Repo size
42.1 GB
Likes
1
Public
Click a slice to open those files.
.gguf42.1 GB · 100%
From the Hugging Face model README
Gliese-CUA-Tool-Call-8B is a Computer Use Agent (CUA) multimodal model based on Qwen2.5-VL-7B-Instruct, designed for GUI understanding, UI localization, and action execution across web, desktop, and mobile environments. It focuses on visual grounding, intent driven actioning, and UI based question answering (VQA), enabling reliable interaction with real world software interfaces. The model is optimized for agentic tool calling, producing structured actions that can be directly executed by downstream systems.
Localization-8B/**
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Gliese-CUA-Tool-Call-8B-Localization.BF16.gguf | BF16 | 15.2 GB | Download |
| Gliese-CUA-Tool-Call-8B-Localization.F16.gguf | F16 | 15.2 GB | Download |
| Gliese-CUA-Tool-Call-8B-Localization.Q8_0.gguf | Q8_0 | 8.1 GB | Download |
| Gliese-CUA-Tool-Call-8B-Localization.mmproj-bf16.gguf | mmproj-bf16 | 1.36 GB | Download |
| Gliese-CUA-Tool-Call-8B-Localization.mmproj-f16.gguf | mmproj-f16 | 1.35 GB | Download |
| Gliese-CUA-Tool-Call-8B-Localization.mmproj-q8_0.gguf | mmproj-q8_0 | 856 MB | Download |
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
