Downloads · 30 days
206
15% of all-time downloads
prithivMLmods/KAIROS-MM-Qwen2.5-VL-SFT-GGUF
KAIROS-MM-Qwen2.5-VL-SFT-GGUF is a video-text-to-text model from prithivMLmods. Use it for the video-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.
KAIROS-MM-Qwen2.5-VL-7B-SFT is a multimodal reasoning model based on Qwen2.5-VL, designed to enable robots and vision AI agents to reason about the real world using physics understanding, prior knowledge, and common s…
Downloads · 30 days
206
15% of all-time downloads
All-time downloads
1.4K
Public
Repo size
40.8 GB
Likes
1
Public
Click a slice to open those files.
.gguf42.1 GB · 100%
From the Hugging Face model README
KAIROS-MM-Qwen2.5-VL-7B-SFT is a multimodal reasoning model based on Qwen2.5-VL, designed to enable robots and vision AI agents to reason about the real world using physics understanding, prior knowledge, and common sense. The model understands space, time, and fundamental physical principles, and is well suited for planning, decision making, and long horizon video reasoning in embodied and agentic systems. It serves as a planning and reasoning backbone for embodied agents, allowing them to infer what actions to take next based on visual observations, temporal context, and physical constraints.
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| KAIROS-MM-Qwen2.5-VL-SFT.BF16.gguf | BF16 | 15.2 GB | Download |
| KAIROS-MM-Qwen2.5-VL-SFT.F16.gguf | F16 | 15.2 GB | Download |
| KAIROS-MM-Qwen2.5-VL-SFT.Q8_0.gguf | Q8_0 | 8.1 GB | Download |
| KAIROS-MM-Qwen2.5-VL-SFT.mmproj-bf16.gguf | mmproj-bf16 | 1.36 GB | Download |
| KAIROS-MM-Qwen2.5-VL-SFT.mmproj-f16.gguf | mmproj-f16 | 1.36 GB | Download |
| KAIROS-MM-Qwen2.5-VL-SFT.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):
