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We're working so hard to get the models available as soon as possible.
- 7 replies
π€ Open to Collab
Simon Pagezy
pagezyhf
AI & ML interests
Healthcare ML
Recent Activity
updated a dataset about 4 hours ago
hf-azure-internal/model-catalog updated a dataset about 5 hours ago
hf-azure-internal/trending-models-analysis updated a dataset about 11 hours ago
amazon-sagemaker/repository-metadataOrganizations
![[INTERNAL] Hugging Face + Dell's profile picture](https://cdn-avatars.huggingface.co/v1/production/uploads/60f0608166e5701b80ed3f02/otGPyK6OMlrQrkngKw4tJ.png)
reacted to Banaxi-Tech's post with π₯ 20 days ago
We're working so hard to get the models available as soon as possible.
reacted to Quazim0t0's post with π 3 months ago
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5,439
Disabling Gated Access for some of my models today. I will update this post with the list as I go. I had to go back recently and make updates to a lot of models and bit off more than I could chew with managing all the releases. I didn't realize many of you asked for access and I apologize for not accepting your access to the models you were wanting to look at. I don't like to release something fully unless I feel I put what I could into it for the moment. Some models will remain on gated access, but I will now be accepting those who request to view the repo.
Disabled Gated Access:
Quazim0t0/Byrne-VLM-131M - v2 Updates + Training Instructions
Quazim0t0/Byrne-Speech - 12M Tiny Speech model
Quazim0t0/Byrne-ASR-English - 12M Tiny ASR Model
Quazim0t0/Byrne-VE - Byrne-VE β Tiny Self-Distilled Vision Encoder (39M)
Quazim0t0/Positronic-144M - Research Artifact
Quazim0t0/SpikeWhale-SNN-216M - Research Artifact
Quazim0t0/Mycel-LM-79M - Research Artifact
Quazim0t0/Chimera-64M - Research Artifact
Accepting Gated Access Requests (7/9):
https://huggingface.co/Quazim0t0/Wheeler-63M
Also uploaded my Neural Photonic Project:
Three trained nets in series: light interferes through the MZI2.pt optical core (verified 256/256), is measured by the PD.pt neural photodetector (verified 1024/1024), and folded into a single OUTPUT byte by the real ADC8 neural-CPU adder. Every value below is computed end-to-end by the three loaded, verified nets β no analytic formulas.
Demo: https://quazim0t0-neural-photonic-hybrid.hf.space/
Model Weights: Quazim0t0/neural-photonic
AND!
A work in progress:
Ashen Depths
https://quazim0t0-ashendepths.static.hf.space/index.html
Disabled Gated Access:
Quazim0t0/Byrne-VLM-131M - v2 Updates + Training Instructions
Quazim0t0/Byrne-Speech - 12M Tiny Speech model
Quazim0t0/Byrne-ASR-English - 12M Tiny ASR Model
Quazim0t0/Byrne-VE - Byrne-VE β Tiny Self-Distilled Vision Encoder (39M)
Quazim0t0/Positronic-144M - Research Artifact
Quazim0t0/SpikeWhale-SNN-216M - Research Artifact
Quazim0t0/Mycel-LM-79M - Research Artifact
Quazim0t0/Chimera-64M - Research Artifact
Accepting Gated Access Requests (7/9):
https://huggingface.co/Quazim0t0/Wheeler-63M
Also uploaded my Neural Photonic Project:
Three trained nets in series: light interferes through the MZI2.pt optical core (verified 256/256), is measured by the PD.pt neural photodetector (verified 1024/1024), and folded into a single OUTPUT byte by the real ADC8 neural-CPU adder. Every value below is computed end-to-end by the three loaded, verified nets β no analytic formulas.
Demo: https://quazim0t0-neural-photonic-hybrid.hf.space/
Model Weights: Quazim0t0/neural-photonic
AND!
A work in progress:
Ashen Depths
https://quazim0t0-ashendepths.static.hf.space/index.html
reacted to ehcalabres's post with π€―π§ β€οΈ 9 months ago
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π Hello HF Posts World!
I'm excited to share in my first HF post that we at Neuraptic AI have released MAGNUM, the first open-source AI model designed to natively support any structured and unstructured data modality.
MAGNUM can learn a holistic representation of your business logic from any source of digital informationβbe it images, documents, emails, databases, audio, signals, and more. This rich context empowers it to deliver significantly more accurate answers.
If you want to know more about it, feel free to ask or read the paper here π€
A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data (2403.04866)
Have a nice week!
I'm excited to share in my first HF post that we at Neuraptic AI have released MAGNUM, the first open-source AI model designed to natively support any structured and unstructured data modality.
MAGNUM can learn a holistic representation of your business logic from any source of digital informationβbe it images, documents, emails, databases, audio, signals, and more. This rich context empowers it to deliver significantly more accurate answers.
If you want to know more about it, feel free to ask or read the paper here π€
A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data (2403.04866)
Have a nice week!
reacted to abidlabs's post with πβ€οΈ 9 months ago
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Open Models vs. Closed APIs for Software Engineers
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If you're an ML researcher / scientist, you probably don't need much convincing to use open models instead of closed APIs -- open models give you reproducibility and let you deeply investigate the model's behavior.
But what if you are a software engineer building products on top of LLMs? I'd argue that open models are a much better option even if you are using them as APIs. For at least 3 reasons:
1) The most obvious reason is reliability of your product. Relying on a closed API means that your product has a single point-of-failure. On the other hand, there are at least 7 different API providers that offer Llama3 70B already. As well as libraries that abstract on top of these API providers so that you can make a single request that goes to different API providers depending on availability / latency.
2) Another benefit is eventual consistency going local. If your product takes off, it will be more economical and lower latency to have a dedicated inference endpoint running on your VPC than to call external APIs. If you've started with an open-source model, you can always deploy the same model locally. You don't need to modify prompts or change any surrounding logic to get consistent behavior. Minimize your technical debt from the beginning.
3) Finally, open models give you much more flexibility. Even if you keep using APIs, you might want to tradeoff latency vs. cost, or use APIs that support batches of inputs, etc. Because different API providers have different infrastructure, you can use the API provider that makes the most sense for your product -- or you can even use multiple API providers for different users (free vs. paid) or different parts of your product (priority features vs. nice-to-haves)
-----------------------------------------------------------------------
If you're an ML researcher / scientist, you probably don't need much convincing to use open models instead of closed APIs -- open models give you reproducibility and let you deeply investigate the model's behavior.
But what if you are a software engineer building products on top of LLMs? I'd argue that open models are a much better option even if you are using them as APIs. For at least 3 reasons:
1) The most obvious reason is reliability of your product. Relying on a closed API means that your product has a single point-of-failure. On the other hand, there are at least 7 different API providers that offer Llama3 70B already. As well as libraries that abstract on top of these API providers so that you can make a single request that goes to different API providers depending on availability / latency.
2) Another benefit is eventual consistency going local. If your product takes off, it will be more economical and lower latency to have a dedicated inference endpoint running on your VPC than to call external APIs. If you've started with an open-source model, you can always deploy the same model locally. You don't need to modify prompts or change any surrounding logic to get consistent behavior. Minimize your technical debt from the beginning.
3) Finally, open models give you much more flexibility. Even if you keep using APIs, you might want to tradeoff latency vs. cost, or use APIs that support batches of inputs, etc. Because different API providers have different infrastructure, you can use the API provider that makes the most sense for your product -- or you can even use multiple API providers for different users (free vs. paid) or different parts of your product (priority features vs. nice-to-haves)
reacted to victor's post with π₯ 10 months ago
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3,647
Nvidia is on a roll lately. Nemotron 3 Nano is my new fav local model, but here's the real flex: they published the entire evaluation setup. Configs, prompts, logs, all of it. This is how you do open models π₯
https://huggingface.co/blog/nvidia/nemotron-3-nano-evaluation-recipe
https://huggingface.co/blog/nvidia/nemotron-3-nano-evaluation-recipe
reacted to sergiopaniego's post with π₯ 12 months ago
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3,163
Sharing the slides from yesterday's talk about "Fine Tuning with TRL" from the @TogetherAgent x @huggingface workshop we hosted in our Paris office π!
Link: https://github.com/sergiopaniego/talks/blob/main/fine_tuning_with_trl/Fine%20tuning%20with%20TRL%20(Oct%2025).pdf
Link: https://github.com/sergiopaniego/talks/blob/main/fine_tuning_with_trl/Fine%20tuning%20with%20TRL%20(Oct%2025).pdf
replied to their post 12 months ago
posted an update 12 months ago
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3,126
π Big news for AI builders!
Weβre thrilled to announce that the Qwen3-VL family of vision-language models is now available on Azure AI Foundry, thanks to our collaboration with Microsoft.
We bring open-source innovation to enterprise-grade AI infrastructure, making it easier than ever for enterprise to deploy and scale the latest and greatest from models from hugging Face securely within Azure.
π Highlights:
- Deploy Qwen3-VL instantly via managed endpoints
- Built-in governance, telemetry, and lifecycle management
- True multimodal reasoning β vision, language, and code understanding
- State-of-the-art performance, outperforming closed-source models like Gemini 2.5 Pro and GPT-5
- Available in both *Instruct* and *Thinking* modes, across 24 model sizes
π Get started today: search for Qwen3-VL in the Hugging Face Collection on Azure AI Foundry.
Weβre thrilled to announce that the Qwen3-VL family of vision-language models is now available on Azure AI Foundry, thanks to our collaboration with Microsoft.
We bring open-source innovation to enterprise-grade AI infrastructure, making it easier than ever for enterprise to deploy and scale the latest and greatest from models from hugging Face securely within Azure.
π Highlights:
- Deploy Qwen3-VL instantly via managed endpoints
- Built-in governance, telemetry, and lifecycle management
- True multimodal reasoning β vision, language, and code understanding
- State-of-the-art performance, outperforming closed-source models like Gemini 2.5 Pro and GPT-5
- Available in both *Instruct* and *Thinking* modes, across 24 model sizes
π Get started today: search for Qwen3-VL in the Hugging Face Collection on Azure AI Foundry.
- 1 reply
reacted to sourceoftruthdata's post with β€οΈ 12 months ago
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3,477
What a fantastic community!
- 1 reply
replied to their post about 1 year ago
Thanks for your answer @nroggendorff !
yeah indeed I think we try to cover the 1 repo = 1 arch use case mostly.
By curiosity, why don't you create multiple repos?
reacted to tsungyi's post with π₯ about 1 year ago
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3,769
Weβre excited to share that Cosmos Reason has surpassed 1 million downloads on Hugging Face!
Cosmos Reason is an open, customizable, commercial-ready 7B-parameter reasoning vision language model (VLM) designed for physical AI. By combining physics understanding, prior knowledge, and common sense reasoning, Cosmos Reason empowers AI agents and robots to operate intelligently in real-world environments.
Key applications already unlocked include:
β Automating large-scale dataset curation and annotation
π€ Powering robot planning and vision-language action (VLA) decision-making
π Driving advanced video analytics and actionable insight generation
Weβre proud to see a global community of developers using Cosmos Reason to teach robots to think like humansβand weβre just getting started.
β‘ Get started with Cosmos Reason 1 NIM, an easy-to-use microservice for AI model deployment: https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/cosmos-reason1-7b?version=1
π See the leaderboard: facebook/physical_reasoning_leaderboard
Cosmos Reason is an open, customizable, commercial-ready 7B-parameter reasoning vision language model (VLM) designed for physical AI. By combining physics understanding, prior knowledge, and common sense reasoning, Cosmos Reason empowers AI agents and robots to operate intelligently in real-world environments.
Key applications already unlocked include:
β Automating large-scale dataset curation and annotation
π€ Powering robot planning and vision-language action (VLA) decision-making
π Driving advanced video analytics and actionable insight generation
Weβre proud to see a global community of developers using Cosmos Reason to teach robots to think like humansβand weβre just getting started.
β‘ Get started with Cosmos Reason 1 NIM, an easy-to-use microservice for AI model deployment: https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/cosmos-reason1-7b?version=1
π See the leaderboard: facebook/physical_reasoning_leaderboard
replied to nroggendorff's post about 1 year ago
Good catch, thanks for reporting!
posted an update about 1 year ago
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887
Whatβs your biggest headache deploying Hugging Face models to the cloudβand how can we fix it for you?
- 8 replies
replied to their post about 1 year ago
posted an update about 1 year ago
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4,014
π€ Collaborating with AMD to ensure Hugging Face Transformers runs smoothly on AMD GPUs!
We run daily CI on AMD MI325 to track the health of the most important model architectures and weβve just made our internal dashboard public.
By making this easily accessible, we hope to spark community contributions and improve support for everyone!
We run daily CI on AMD MI325 to track the health of the most important model architectures and weβve just made our internal dashboard public.
By making this easily accessible, we hope to spark community contributions and improve support for everyone!
- 2 replies
reacted to jeffboudier's post with π₯ about 1 year ago
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3,474
Quick 30s demo of the new Hub > Azure AI integration to deploy HF models in your own Azure account. Now with Py and CLI!
GG @alvarobartt @kramp @pagezyhf
GG @alvarobartt @kramp @pagezyhf