Hugging Face: Difference between revisions
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=== What is Hugging Face? === | === What is Hugging Face? === | ||
Hugging Face is a platform sharing tools, models, datasets, focused on democratizing AI. It is a | Hugging Face is a platform sharing tools, models, datasets, focused on democratizing AI. It is a collaborative hub for an AI community. It is not specifically targeted AI image creation, but generative AI more broadly. Hugging Face is a platform, but what it offers is more resembling an infrastructure for, in particular, training models. Stability AI, and also others involved in training foundation models would typically have their own infrastructures, but they may make them available on Hugging Face, allowing the platform users to 'post train' and create LoRAs, for instance. It also allows users to experiment in other ways, for instance to create 'pipelines' of models, meaning that the outcome of one model can become the input for another model. In this sense, it can also be compared to platforms such as Github, and what it really offers is an infrastructure, and not least an expertise, in handling all of this at a very large scale, involving | ||
Complex pipelines .. . you have access meaning you can have one model - outpost for another - | |||
Foundation models training - customised set up / aim for smth that cannot be replicated | |||
… value of HF . Pipeline of models / experimentation with | |||
For instance, one might find a dataset for creating 2-D, cartoon-line video game characters. | |||
Access to, and not least integration of models is a complicated issue that demands high technical expertise. More than anything, what Hugging Face offers is therefore an infrastructure. | Access to, and not least integration of models is a complicated issue that demands high technical expertise. More than anything, what Hugging Face offers is therefore an infrastructure. | ||
Revision as of 13:32, 8 July 2025
Hugging Face is a platform sharing tools, models, datasets, focused on democratizing AI. It is a sort of collaborative hub for an AI community, but it is also registered as a private company teaming up with, for instance, Meta to boost European startups in an "AI Accelerator Program". The company also collaborates with Amazon Web Services to allow users to deploy the trained and publicly available models in Hugging Face through Amazon SageMaker. Nasdaq Private Market lists a whole range of investors in Hugging Face (Amazon, Google, Intel, IBM, NVIDIA, etc.), and its estimated market value was $4.5 billion in 2023, which of course also reflects the high expertise the company has in managing models, hosting them, and making them available.
What is Hugging Face?
Hugging Face is a platform sharing tools, models, datasets, focused on democratizing AI. It is a collaborative hub for an AI community. It is not specifically targeted AI image creation, but generative AI more broadly. Hugging Face is a platform, but what it offers is more resembling an infrastructure for, in particular, training models. Stability AI, and also others involved in training foundation models would typically have their own infrastructures, but they may make them available on Hugging Face, allowing the platform users to 'post train' and create LoRAs, for instance. It also allows users to experiment in other ways, for instance to create 'pipelines' of models, meaning that the outcome of one model can become the input for another model. In this sense, it can also be compared to platforms such as Github, and what it really offers is an infrastructure, and not least an expertise, in handling all of this at a very large scale, involving
Complex pipelines .. . you have access meaning you can have one model - outpost for another -
Foundation models training - customised set up / aim for smth that cannot be replicated
… value of HF . Pipeline of models / experimentation with
For instance, one might find a dataset for creating 2-D, cartoon-line video game characters.
Access to, and not least integration of models is a complicated issue that demands high technical expertise. More than anything, what Hugging Face offers is therefore an infrastructure.
I had used this same image set for some experimental tests on Stable Diffusion XL via LORA and Dreambooth training methods for some solid results post-training
Upload datasets - train models
.... training models at large scale
+ customisation for clients / manually racks of machines
Upload a dataset / accesss for others to download
But also to access models on HF
https://huggingface.co/datasets/mgane/2D_Video_Game_Cartoon_Character_Sprite-Sheets
"mgane/2D_Video_Game_Cartoon_Character_Sprite-Sheets" - a "76 cartoon art-style video game character spritesheets"
"All images editted using Tiled image editting software as most assets are typically downloaded individually and not in sequence. I compiled each animation sequence into one img to display animations frame-by-frame evenly distributed across some common animations seen in 2D video game art (Idle, Attack, Walk, Running, etc). I had used this same image set for some experimental tests on Stable Diffusion XL via LORA and Dreambooth training methods for some solid results post-training."
https://huggingface.co/datasets/mgane/2D_Video_Game_Cartoon_Character_Sprite-Sheets
but it is also registered as a private company teaming up with, for instance, Meta to boost European startups in an "AI Accelerator Program". The company also collaborates with Amazon Web Services to allow users to deploy the trained and publicly available models in Hugging Face through Amazon SageMaker. Nasdaq Private Market lists a whole range of investors in Hugging Face (Amazon, Google, Intel, IBM, NVIDIA, etc.), and its estimated market value was $4.5 billion in 2023, which of course also reflects the high expertise the company has in managing models, hosting them, and making them available.
What is the network that sustains this object?
- How does it move from person to person, person to software, to platform, what things are attached to it (visual culture)
- Networks of attachments
- How does it relate / sustain a collective? (human + non-human)
How does it evolve through time?
Evolution of the interface for these objects. Early chatgpt offered two parameters through the API: prompt and temperature. Today extremely complex object with all kinds of components and parameters. Visually what is the difference? Richness of the interface in decentralization (the more options, the better...)
