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AI 모델 84759
- ALMA-7B-Pretrain-GGUF↗
TheBloke
- ShiningValiantXS-1.1-GGUF↗
TheBloke
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
- caformer_m36.sail_in1k↗
timm
- glm-4↗
tiny-random
- Arch-Router-1.5B-GGUF↗
tinyopsec
- Qwen3.5-2B-Base↗
titan0115
- Kev-27B-Artemis-GGUF↗
turintech
Kev-27B for routing, triage and quick checks on a single 24 GB GPU. It answers typed questions (yes or no, pick one, a score) with a probability for each answer, in one pass. This is a decision model: send typed questions to llama-server's `/v1/systemone` endpoint, not to chat. Tested with llama.cpp commit `fcac190` (13 October 2026); llama.cpp serves Kev models since 9 October 2026. Tested on an Apple M3 Max (36 GB) with llama.cpp Metal (`b11617`): every file here loads and answers with the command above.
- Kimi-K3-Abliterated-V1↗
Uniboshi
- gemma-3n-E4B-it-unsloth-bnb-4bit↗
unsloth
- Qwen2.5-VL-7B-Instruct-bnb-4bit↗
unsloth
In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments. We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.