C/C++ — https://github.com/ggml-org/llama.cpp
Search public pages, research tools, and SAVRN solutions.
Open-weight model · Video text to text
by Skycontact SkyContact/MOSS-VL-Realtime-GGUF
MOSS-VL-Realtime-GGUF is an open-weight model for video text to text from Skycontact, released under Apache License 2.0. Its published files total 8.4 GB.
GGUF quantization of OpenMOSS-Team/MOSS-VL-Realtime-NF4 (11B cross-attention VLM: Qwen3 text trunk with 12 pure cross-attn layers + SigLIP tower with deepstack), repackaged for the SkyVision.cpp inference engine.
By Skycontact, published under apache-2.0, revision dca2156a7a19.
GGUF quantization of OpenMOSS-Team/MOSS-VL-Realtime-NF4 (11B cross-attention VLM: Qwen3 text trunk with 12 pure cross-attn layers + SigLIP tower with deepstack), repackaged for the SkyVision.cpp inference engine. - NF4 weights are dequantized bit-exactly against bitsandbytes CUDA first (checkmossvlnf4.py); - the fused cross-KV projection weight is built at load time (engine-side); 12 GB GPU deployments: --n-ctx 4096 (and MOSSXCTX=6144 for long videos). Full chat, multi-turn sessions, realtime streaming (proactive narration with ) and a web UI are available via --serve. Follows the base model's license (see the OpenMOSS-Team/MOSS-VL-Realtime-NF4 page).
GGUF quantization of OpenMOSS-Team/MOSS-VL-Realtime-NF4 (11B cross-attention VLM: Qwen3 text trunk with 12 pure cross-attn layers + SigLIP tower with deepstack), repackaged for the SkyVision.cpp inference engine.
Built for SkyVision.cpp only. The GGUF declares
general.architecture = moss_vl; llama.cpp / ollama / LM Studio and other generic GGUF runtimes cannot load it. This repository is not part of the generic GGUF ecosystem - do not use it as a signal of whether the model "works".
| File | Size | Notes |
|---|---|---|
Moss-VL-Realtime-q4k.gguf |
8.4 GB | NF4 → Q4_K (linears) + Q6_K (norm-adjacent) + F16 (vision), fits a 12 GB GPU |
check_moss_vl_nf4.py);vqa-cli --model Moss-VL-Realtime-q4k.gguf --image photo.jpg --ask "Describe this image."
vqa-cli --model Moss-VL-Realtime-q4k.gguf --video clip.mp4 --max-frames 8 --ask "What happens?"
12 GB GPU deployments: --n-ctx 4096 (and MOSS_XCTX=6144 for long videos). Full chat, multi-turn sessions, realtime streaming (proactive narration with <|silence|>) and a web UI are available via --serve.
Follows the base model's license (see the OpenMOSS-Team/MOSS-VL-Realtime-NF4 page).
3 files, 8.4 GB in total. The weights are 1 file totalling 8.4 GB in gguf.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| Moss-VL-Realtime-q4k.gguf | Weights | 8.4 GB | 605470f81a2e |
| README.md | Documentation | 2.2 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
Released by Skycontact through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 8.4 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. MOSS-VL-Realtime-GGUF is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
C/C++ — https://github.com/ggml-org/llama.cpp
C/C++ — https://github.com/ggml-org/llama.cpp
EventLens-4B is a video-language model developed through event-structure reinforcement learning and multi-teacher on-policy distillation (MT-OPD). Built on Qwen3-VL-4B-Instruct, it learns to recognize event boundaries, progression, and relations, alongside temporal grounding and video question answering. The model is part of EventLens: Event-Structure Reinforcement Learning for Video Understanding, developed by AIM3 Lab, Renmin University of China, and Kling AI. Selected results reported in the EventLens paper, compared with the corresponding pretrained backbone under the shared 256-frame protocol. QVHighlights, ActivityNet, and Charades-STA use mIoU. E.T. Bench and VinoGround use their…
EventLens-8B is a video-language model developed through event-structure reinforcement learning and multi-teacher on-policy distillation (MT-OPD). Built on Qwen3-VL-8B-Instruct, it learns to recognize event boundaries, progression, and relations, alongside temporal grounding and video question answering. The model is part of EventLens: Event-Structure Reinforcement Learning for Video Understanding, developed by AIM3 Lab, Renmin University of China, and Kling AI. Selected results reported in the EventLens paper, compared with the corresponding pretrained backbone under the shared 256-frame protocol. QVHighlights, ActivityNet, and Charades-STA use mIoU. E.T. Bench and VinoGround use their…