BEACON: A Multimodal Dataset for Learning Behavioral Fingerprints from Gameplay Data
Paper • 2605.10867 • Published • 2
Uday Pratap Singh Atwal
upsatwal
AI & ML interests
Inference optimization, ML Ops
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authored a paper about 13 hours ago
BEACON: A Multimodal Dataset for Learning Behavioral Fingerprints from Gameplay Data updated a dataset 1 day ago
beacon-gui/BEACON-DatasetOrganizations
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Comparing: DeepCaption-VLA-7B, built on Qwen2.5-VL-7B-Instruct, is tailored for image captioning and vision-language attribution, focusing on precise, descriptive captions of visual properties, object attributes, and scene details. In contrast, Qwen2.5-VL-7B-Abliterated-Caption-it is fine-tuned for abliterated captioning, generating highly detailed descriptions across diverse visual categories.
Models🤗
✦ DeepCaption-VLA-7B : prithivMLmods/DeepCaption-VLA-7B
✦ Qwen2.5-VL-7B-Abliterated-Caption-it : prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it
Spaces⛵
➜ VisionScope-R2 : prithivMLmods/VisionScope-R2
➜ Qwen2.5-VL-Outpost : https://huggingface.co/spaces/prithivMLmods/Qwen2.5-VL-Outpost
Collection🗞️
Multimodal VLMs - Aug'25 : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027
GitHub↗️
> DeepCaption-VLA-7B [4bit-notebook demo] : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/DeepCaption-VLA-7B%5B4bit%20-%20notebook%20demo%5D/DeepCaption-VLA-7B.ipynb
> Qwen2.5-VL-3B-Abliterated-Caption-it(caption) : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/Qwen2.5-VL-3B-Abliterated-Caption-it(caption)/Qwen2_5_VL_3B_Abliterated_Caption_it.ipynb
The community GPU grant was given by Hugging Face — special thanks to them. 🤗🚀
To know more about it, visit the app page or the respective model page!!
Models🤗
✦ DeepCaption-VLA-7B : prithivMLmods/DeepCaption-VLA-7B
✦ Qwen2.5-VL-7B-Abliterated-Caption-it : prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it
Spaces⛵
➜ VisionScope-R2 : prithivMLmods/VisionScope-R2
➜ Qwen2.5-VL-Outpost : https://huggingface.co/spaces/prithivMLmods/Qwen2.5-VL-Outpost
Collection🗞️
DeepCaption attr. : prithivMLmods/deepcaption-attr-68b041172ebcb867e45c556aVL Abliterated-Caption : prithivMLmods/vl-abliterated-caption-68a0443b63182e97a15c47a3Multimodal VLMs - Until July'25 : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027Multimodal VLMs - Aug'25 : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027
GitHub↗️
> DeepCaption-VLA-7B [4bit-notebook demo] : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/DeepCaption-VLA-7B%5B4bit%20-%20notebook%20demo%5D/DeepCaption-VLA-7B.ipynb
> Qwen2.5-VL-3B-Abliterated-Caption-it(caption) : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/Qwen2.5-VL-3B-Abliterated-Caption-it(caption)/Qwen2_5_VL_3B_Abliterated_Caption_it.ipynb
The community GPU grant was given by Hugging Face — special thanks to them. 🤗🚀
To know more about it, visit the app page or the respective model page!!
reacted to Lewdiculous's post with ❤️ 8 months ago
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More context for your Pascal GPU or older!
Update: Now available in the official releases of KoboldCpp!
[releases] https://github.com/LostRuins/koboldcpp/releases/latest
These are great news for all the users with GTX 10XX, P40...
Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama.cpp in the last few days, and should be merged in the next version of KoboldCpp, you can already try it with another fork or by building it.
[Mentioned KCPP fork] https://github.com/Nexesenex/kobold.cpp/releases/latest
[PR] https://github.com/ggerganov/llama.cpp/pull/7188
You should expect less VRAM usage for the same context, allowing you to experience higher contexts with your current GPU.
There have also been reported final tokens/second speed improvements for inference, so that's also grand!
If you have tried it, I'd like to hear your experiences with --flashattention so far, especially for this implementation and for the large number of Pascal (GTX 10XX, P40...) cards.
Discussion linked bellow, with more links to relevant information:
https://huggingface.co/LWDCLS/LLM-Discussions/discussions/11
Cheers!
Update: Now available in the official releases of KoboldCpp!
[releases] https://github.com/LostRuins/koboldcpp/releases/latest
These are great news for all the users with GTX 10XX, P40...
Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama.cpp in the last few days, and should be merged in the next version of KoboldCpp, you can already try it with another fork or by building it.
[Mentioned KCPP fork] https://github.com/Nexesenex/kobold.cpp/releases/latest
[PR] https://github.com/ggerganov/llama.cpp/pull/7188
You should expect less VRAM usage for the same context, allowing you to experience higher contexts with your current GPU.
There have also been reported final tokens/second speed improvements for inference, so that's also grand!
If you have tried it, I'd like to hear your experiences with --flashattention so far, especially for this implementation and for the large number of Pascal (GTX 10XX, P40...) cards.
Discussion linked bellow, with more links to relevant information:
https://huggingface.co/LWDCLS/LLM-Discussions/discussions/11
Cheers!
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