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filvyb/llama-joycaption-beta-one-hf-llava-GGUF overview

Model Card for Llama JoyCaption Beta One Github https://github.com/fpgaminer/joycaption JoyCaption is an image captioning Visual Language Model VLM built from …

transformersggufcaptioningimage-text-to-textbase_model:fancyfeast/llama-joycaption-beta-one-hf-llavabase_model:quantized:fancyfeast/llama-joycaption-beta-one-hf-llavalicense:llama3.1endpoints_compatibleregion:usconversational

Runs locally from ~565.4 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
33
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Pipeline
image-text-to-text
Author

Repository Files & Downloads

4 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
llama-joycaption-beta-one.Q6_K.ggufGGUFGGUF6.14 GBDownload
llama-joycaption-beta-one.Q8_0.ggufGGUFGGUF7.95 GBDownload
llama-joycaption-beta-one.mmproj-Q8_0.ggufGGUFQ8_0565.4 MBDownload
llama-joycaption-beta-one.mmproj-f16.ggufGGUFF16831.1 MBDownload

Model Details

Model IDfilvyb/llama-joycaption-beta-one-hf-llava-GGUF
Authorfilvyb
Pipelineimage-text-to-text
Licensellama3.1
Base modelfancyfeast/llama-joycaption-beta-one-hf-llava
Last modified2026-09-16T17:19:57.000Z

Model README

---

license: llama3.1

base_model:

  • fancyfeast/llama-joycaption-beta-one-hf-llava

tags:

  • captioning

pipeline_tag: image-text-to-text

library_name: transformers

---

Model Card for Llama JoyCaption Beta One

Github

JoyCaption is an image captioning Visual Language Model (VLM) built from the ground up as a free, open, and uncensored model for the community to use in training Diffusion models.

Key Features:

  • Free and Open: Always released for free, open weights, no restrictions, and just like bigASP, will come with training scripts and lots of juicy details on how it gets built.
  • Uncensored: Equal coverage of SFW and NSFW concepts. No "cylindrical shaped object with a white substance coming out on it" here.
  • Diversity: All are welcome here. Do you like digital art? Photoreal? Anime? Furry? JoyCaption is for everyone. Pains are being taken to ensure broad coverage of image styles, content, ethnicity, gender, orientation, etc.
  • Minimal Filtering: JoyCaption is trained on large swathes of images so that it can understand almost all aspects of our world. almost. Illegal content will never be tolerated in JoyCaption's training.

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