Share and discover more about AI with social posts from the community.huggingface/OpenAi
Build real-time serverless video, audio and data applications.
Cloudflare Calls is infrastructure for real-time audio/video/data applications. It allows you to build real-time apps without worrying about scaling or regions. It can act as a selective forwarding unit (WebRTC SFU), as a fanout delivery system for broadcasting (WebRTC CDN) or anything in between.
Cloudflare Calls runs on Cloudflare’s global cloud network in hundreds of cities worldwide.https://developers.cloudflare.com/calls/
Cloudflare Calls is infrastructure for real-time audio/video/data applications. It allows you to build real-time apps without worrying about scaling or regions. It can act as a selective forwarding unit (WebRTC SFU), as a fanout delivery system for broadcasting (WebRTC CDN) or anything in between.
Cloudflare Calls runs on Cloudflare’s global cloud network in hundreds of cities worldwide.https://developers.cloudflare.com/calls/
Cloudflare Docs
Overview | Cloudflare Calls docs
Cloudflare Calls is infrastructure for real-time audio/video/data applications. It allows you to build real-time apps without worrying about scaling or regions. It can act as a selective forwarding unit (WebRTC SFU), as a fanout delivery system for broadcasting…
Cloudflare for Platforms
Cloudflare’s offering for SaaS businesses.
Extend Cloudflare’s security, reliability, and performance services to your customers with Cloudflare for Platforms. Together with Cloudflare for SaaS and Workers for Platforms, your customers can build custom logic to meet their needs right into your application.
Products
Cloudflare for SaaS
Cloudflare for SaaS allows you to extend the security and performance benefits of Cloudflare’s network to your customers via their own custom or vanity domains.
Use Cloudflare for SaaS
Workers for Platforms
Workers for Platforms help you deploy serverless functions programmatically on behalf of your customers.
Use Workers for Platforms
https://developers.cloudflare.com/cloudflare-for-platforms/
Cloudflare’s offering for SaaS businesses.
Extend Cloudflare’s security, reliability, and performance services to your customers with Cloudflare for Platforms. Together with Cloudflare for SaaS and Workers for Platforms, your customers can build custom logic to meet their needs right into your application.
Products
Cloudflare for SaaS
Cloudflare for SaaS allows you to extend the security and performance benefits of Cloudflare’s network to your customers via their own custom or vanity domains.
Use Cloudflare for SaaS
Workers for Platforms
Workers for Platforms help you deploy serverless functions programmatically on behalf of your customers.
Use Workers for Platforms
https://developers.cloudflare.com/cloudflare-for-platforms/
Cloudflare Docs
Cloudflare for Platforms | Cloudflare for Platforms docs
Extend Cloudflare’s security, reliability, and performance services to your customers with Cloudflare for Platforms. Together with Cloudflare for SaaS and Workers for Platforms, your customers can build custom logic to meet their needs right into your application.
The Cloudflare China Network is a package of selected Cloudflare’s performance and security products running on data centers located in mainland China and operated by Cloudflare’s partner JD Cloud.
The data centers cover most populated regions in China. Combining Cloudflare’s technological leadership and JD Cloud’s local operations expertise, the Cloudflare China Network is designed to meet the needs for secure, reliable, and fast-performing content delivery in China. You can use the same configurations that you use with Cloudflare everywhere else in the world and with the same dashboard experience.
Main features
The Cloudflare China Network provides:
A single solution for both performance improvement and security services such as WAF, DDoS, and bot management.
An unified experience for managing network traffic and security posture. You can manage all configurations on the same dashboard.
The same customer support capabilities as Cloudflare’s global network. You may also have access to premium service and local language support.https://developers.cloudflare.com/china-network/
The data centers cover most populated regions in China. Combining Cloudflare’s technological leadership and JD Cloud’s local operations expertise, the Cloudflare China Network is designed to meet the needs for secure, reliable, and fast-performing content delivery in China. You can use the same configurations that you use with Cloudflare everywhere else in the world and with the same dashboard experience.
Main features
The Cloudflare China Network provides:
A single solution for both performance improvement and security services such as WAF, DDoS, and bot management.
An unified experience for managing network traffic and security posture. You can manage all configurations on the same dashboard.
The same customer support capabilities as Cloudflare’s global network. You may also have access to premium service and local language support.https://developers.cloudflare.com/china-network/
Cloudflare Docs
Overview | Cloudflare China Network docs
The Cloudflare China Network ↗ is a package of selected Cloudflare’s performance and security products running on data centers located in mainland China and operated by Cloudflare’s partner JD Cloud.
Speed up your online experience with Cloudflare’s public DNS resolver.
Available on all plans
1.1.1.1 is Cloudflare’s public DNS resolver. It offers a fast and private way to browse the Internet. DNS resolvers translate domains like cloudflare.com into the IP addresses necessary to reach the website (like 104.16.123.96).
Unlike most DNS resolvers, 1.1.1.1 does not sell user data to advertisers. 1.1.1.1 has also been measured to be the fastest DNS resolver available — it is deployed in hundreds of cities worldwide, and has access to the addresses of millions of domain names on the same servers it runs on.
1.1.1.1 is completely free. Setting it up takes minutes and requires no special software.https://developers.cloudflare.com/1.1.1.1/
Available on all plans
1.1.1.1 is Cloudflare’s public DNS resolver. It offers a fast and private way to browse the Internet. DNS resolvers translate domains like cloudflare.com into the IP addresses necessary to reach the website (like 104.16.123.96).
Unlike most DNS resolvers, 1.1.1.1 does not sell user data to advertisers. 1.1.1.1 has also been measured to be the fastest DNS resolver available — it is deployed in hundreds of cities worldwide, and has access to the addresses of millions of domain names on the same servers it runs on.
1.1.1.1 is completely free. Setting it up takes minutes and requires no special software.https://developers.cloudflare.com/1.1.1.1/
Cloudflare Docs
Overview | Cloudflare 1.1.1.1 docs
1.1.1.1 is Cloudflare’s public DNS resolver. It offers a fast and private way to browse the Internet. DNS resolvers ↗ translate domains like cloudflare.com into the IP addresses necessary to reach the website (like 104.16.123.96).
Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources: tinystories_all_data.tar.gz - contains a superset of the stories together with metadata and the prompt that was used to create each story.
TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger).
Evaluation_prompts.yaml: List of prompts used to evaluate our models (see paper)https://huggingface.co/datasets/roneneldan/TinyStories
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources: tinystories_all_data.tar.gz - contains a superset of the stories together with metadata and the prompt that was used to create each story.
TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger).
Evaluation_prompts.yaml: List of prompts used to evaluate our models (see paper)https://huggingface.co/datasets/roneneldan/TinyStories
Dataset Card for Alpaca-Cleaned
Repository: https://github.com/gururise/AlpacaDataCleaned
Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.https://huggingface.co/datasets/yahma/alpaca-cleaned
Repository: https://github.com/gururise/AlpacaDataCleaned
Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.https://huggingface.co/datasets/yahma/alpaca-cleaned
GitHub
GitHub - gururise/AlpacaDataCleaned: Alpaca dataset from Stanford, cleaned and curated
Alpaca dataset from Stanford, cleaned and curated. Contribute to gururise/AlpacaDataCleaned development by creating an account on GitHub.
Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:)
I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun
At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of the key components: a context encoder, target encoder and a predictor.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb
I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun
At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of the key components: a context encoder, target encoder and a predictor.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb
GitHub
ai-algorithms/mnist_ijepa.ipynb at main · Jaykef/ai-algorithms
Contribute to Jaykef/ai-algorithms development by creating an account on GitHub.
Hugging Face Bolsters AI Infrastructure With XetHub Acquisition
Hugging Face, a leading platform for open-source machine learning projects, has made a strategic acquisition of XetHub, a Seattle-based startup specializing in file management for artificial intelligence projects. This move aims to significantly enhance Hugging Face's AI storage capabilities, enabling developers to work with larger models and datasets more efficiently.
XetHub was founded by Yucheng Low, Ajit Banerjee and Rajat Arya, who previously worked at Apple, where they built and scaled Apple's internal ML infrastructure. The founders have a strong background in machine learning and data management, with Yucheng Low having co-founded Turi, a transformative ML/AI company acquired by Apple in 2016.
The startup has successfully raised $7.5 million in seed financing led by Seattle-based venture capital firm Madrona Ventures.https://www.forbes.com/sites/janakirammsv/2024/08/12/hugging-face-bolsters-ai-infrastructure-with-xethub-acquisition/
Hugging Face, a leading platform for open-source machine learning projects, has made a strategic acquisition of XetHub, a Seattle-based startup specializing in file management for artificial intelligence projects. This move aims to significantly enhance Hugging Face's AI storage capabilities, enabling developers to work with larger models and datasets more efficiently.
XetHub was founded by Yucheng Low, Ajit Banerjee and Rajat Arya, who previously worked at Apple, where they built and scaled Apple's internal ML infrastructure. The founders have a strong background in machine learning and data management, with Yucheng Low having co-founded Turi, a transformative ML/AI company acquired by Apple in 2016.
The startup has successfully raised $7.5 million in seed financing led by Seattle-based venture capital firm Madrona Ventures.https://www.forbes.com/sites/janakirammsv/2024/08/12/hugging-face-bolsters-ai-infrastructure-with-xethub-acquisition/
Forbes
Hugging Face Bolsters AI Infrastructure With XetHub Acquisition
Hugging Face's acquisition of XetHub is an important step toward accelerating the adoption of open-weight models.
Hugging Face acquires Seattle data storage startup XetHub
GeekWire’s in-depth startup coverage tells the stories of the Pacific Northwest entrepreneurial scene.
XetHub CEO Yucheng Low. (LinkedIn Photo)
Hugging Face has acquired XetHub, a data storage and collaboration startup founded by former Apple engineers that helped developers streamline the process of building machine learning and artificial intelligence applications.
“Together we share a vision of democratizing AI to enable everyone to host, share, and build models and datasets,” XetHub CEO Yucheng Low wrote on LinkedIn. “At Hugging Face, we will continue to pursue this vision, integrating our technologies into the Hugging Face Hub to create the future of AI collaboration.”
The deal reflects the demand for data storage and compute needs driven by the AI boom, and is the latest in a string of smaller AI startups — and executives from those startups — getting gobbled up by larger companies.
New York-based Hugging Face offers a number of developer tools to help companies test, store, and run large-scale AI models that require substantial compute and storage capabilities. It raised a $235 million Series D round a year ago and acquired another developer tool startup called Argilla for $10 million in June.
Hugging Face isn’t sharing terms of the XetHub deal but says it’s the company’s largest acquisition to date. XetHub will add 14 employees to Hugging Face’s workforce.
Low co-founded Seattle-based XetHub in 2021. He previously worked at Turi, a Seattle machine learning startup that was acquired in 2016 by Apple, where he then spent nearly five years. Low is a graduate of Carnegie Mellon University, where he earned a PhD in machine learning.
XetHub co-founder Rajat Arya also worked at Turi and Apple. He previously worked at Amazon Web Services and Microsoft.
The startup’s third co-founder, Ajit Banerjee, is a former senior software architect at Apple who co-founded a Seattle job interviewing and matching startup called TalentWorks.
“When Amazon recommends a product, or Gmail auto-suggests an email reply, or Apple’s FaceID unlocks a screen: these are examples of intelligent applications,” Arya told GeekWire last year. “Up until now, this AI-powered functionality has been limited to those big players, but increasingly we’re going to see every business adopt AI.”
XetHub raised a $7.5 million round last year from Madrona Ventures.https://www.geekwire.com/2024/hugging-face-acquires-seattle-data-storage-startup-xethub/
GeekWire’s in-depth startup coverage tells the stories of the Pacific Northwest entrepreneurial scene.
XetHub CEO Yucheng Low. (LinkedIn Photo)
Hugging Face has acquired XetHub, a data storage and collaboration startup founded by former Apple engineers that helped developers streamline the process of building machine learning and artificial intelligence applications.
“Together we share a vision of democratizing AI to enable everyone to host, share, and build models and datasets,” XetHub CEO Yucheng Low wrote on LinkedIn. “At Hugging Face, we will continue to pursue this vision, integrating our technologies into the Hugging Face Hub to create the future of AI collaboration.”
The deal reflects the demand for data storage and compute needs driven by the AI boom, and is the latest in a string of smaller AI startups — and executives from those startups — getting gobbled up by larger companies.
New York-based Hugging Face offers a number of developer tools to help companies test, store, and run large-scale AI models that require substantial compute and storage capabilities. It raised a $235 million Series D round a year ago and acquired another developer tool startup called Argilla for $10 million in June.
Hugging Face isn’t sharing terms of the XetHub deal but says it’s the company’s largest acquisition to date. XetHub will add 14 employees to Hugging Face’s workforce.
Low co-founded Seattle-based XetHub in 2021. He previously worked at Turi, a Seattle machine learning startup that was acquired in 2016 by Apple, where he then spent nearly five years. Low is a graduate of Carnegie Mellon University, where he earned a PhD in machine learning.
XetHub co-founder Rajat Arya also worked at Turi and Apple. He previously worked at Amazon Web Services and Microsoft.
The startup’s third co-founder, Ajit Banerjee, is a former senior software architect at Apple who co-founded a Seattle job interviewing and matching startup called TalentWorks.
“When Amazon recommends a product, or Gmail auto-suggests an email reply, or Apple’s FaceID unlocks a screen: these are examples of intelligent applications,” Arya told GeekWire last year. “Up until now, this AI-powered functionality has been limited to those big players, but increasingly we’re going to see every business adopt AI.”
XetHub raised a $7.5 million round last year from Madrona Ventures.https://www.geekwire.com/2024/hugging-face-acquires-seattle-data-storage-startup-xethub/
GeekWire
Hugging Face acquires Seattle data storage startup XetHub
XetHub CEO Yucheng Low. (LinkedIn Photo) Hugging Face has acquired XetHub, a data storage and collaboration startup founded by former Apple engineers that
Hugging Face acquires XetHub from ex-Apple researchers for large AI model hosting
Hugging Face today announced it has acquired Seattle-based XetHub, a collaborative development platform founded by former Apple researchers to help machine learning teams work more efficiently with large datasets and models.
While the exact value of the deal remains undisclosed, CEO Clem Delangue said in an interview with Forbes that this is the largest acquisition the company has made thus far.https://venturebeat.com/ai/hugging-face-acquires-xethub-from-ex-apple-researchers-for-large-ai-model-hosting/
Hugging Face today announced it has acquired Seattle-based XetHub, a collaborative development platform founded by former Apple researchers to help machine learning teams work more efficiently with large datasets and models.
While the exact value of the deal remains undisclosed, CEO Clem Delangue said in an interview with Forbes that this is the largest acquisition the company has made thus far.https://venturebeat.com/ai/hugging-face-acquires-xethub-from-ex-apple-researchers-for-large-ai-model-hosting/
VentureBeat
Hugging Face acquires XetHub from ex-Apple researchers for large AI model hosting
The HF team plans to integrate XetHub and upgrade its storage backend, enabling developers to host more large models and datasets.
NVIDIA NIM Now Available on Hugging Face with Inference-as-a-Service
Hugging Face has announced the launch of an inference-as-a-service capability powered by NVIDIA NIM. This new service will provide developers easy access to NVIDIA-accelerated inference for popular AI models.
The new service allows developers to rapidly deploy leading large language models such as the Llama 3 family and Mistral AI models with optimization from NVIDIA NIM microservices running on NVIDIA DGX Cloud. This will help developers quickly prototype with open-source AI models hosted on the Hugging Face Hub and deploy them in production.
The Hugging Face inference-as-a-service on NVIDIA DGX Cloud powered by NIM microservices offers easy access to compute resources that are optimized for AI deployment. The NVIDIA DGX Cloud platform is purpose-built for generative AI and provides scalable GPU resources that support every step of AI development, from prototype to production.
To use the service, users must have access to an Enterprise Hub organization and a fine-grained token for authentication. The NVIDIA NIM Endpoints for supported Generative AI models can be found on the model page of the Hugging Face Hub.https://www.infoq.com/news/2024/08/nvidia-nim-huggingface/
Hugging Face has announced the launch of an inference-as-a-service capability powered by NVIDIA NIM. This new service will provide developers easy access to NVIDIA-accelerated inference for popular AI models.
The new service allows developers to rapidly deploy leading large language models such as the Llama 3 family and Mistral AI models with optimization from NVIDIA NIM microservices running on NVIDIA DGX Cloud. This will help developers quickly prototype with open-source AI models hosted on the Hugging Face Hub and deploy them in production.
The Hugging Face inference-as-a-service on NVIDIA DGX Cloud powered by NIM microservices offers easy access to compute resources that are optimized for AI deployment. The NVIDIA DGX Cloud platform is purpose-built for generative AI and provides scalable GPU resources that support every step of AI development, from prototype to production.
To use the service, users must have access to an Enterprise Hub organization and a fine-grained token for authentication. The NVIDIA NIM Endpoints for supported Generative AI models can be found on the model page of the Hugging Face Hub.https://www.infoq.com/news/2024/08/nvidia-nim-huggingface/
InfoQ
NVIDIA NIM Now Available on Hugging Face with Inference-as-a-Service
Hugging Face has announced the launch of an inference-as-a-service capability powered by NVIDIA NIM. This new service will provide developers easy access to NVIDIA-accelerated inference for popular AI models.
Serverless Inference API has shorter context length than the model?
I tried Llama 3.1 70B with Huggingface Serverless Inference API but got an error with 20k tokens even if the model has 128k context length. Does Huggingface limit the context length on top of the model and is there any workaround for this?
I tried Llama 3.1 70B with Huggingface Serverless Inference API but got an error with 20k tokens even if the model has 128k context length. Does Huggingface limit the context length on top of the model and is there any workaround for this?
Need Help Integrating black-forest-labs/FLUX.1-dev Text-to-Image Model in Next.js App
I'm trying to build a Next.js app using the black-forest-labs/FLUX.1-dev text-to-image model, but I've been struggling to get it working for the past few days. I've tried using the Next.js AI SDK and the HfInference library, but I'm not sure how to properly integrate them. Has anyone had experience with this or could offer some guidance? Any help would be greatly appreciated!
I'm trying to build a Next.js app using the black-forest-labs/FLUX.1-dev text-to-image model, but I've been struggling to get it working for the past few days. I've tried using the Next.js AI SDK and the HfInference library, but I'm not sure how to properly integrate them. Has anyone had experience with this or could offer some guidance? Any help would be greatly appreciated!
Difficulties to deal with HuggingFace transformers
Hi,
I am currently working with HuggingFace's transformers library. It is somewhat convenient to load models. I am not a troll. But the deeper I go, the more difficulties arise and I got the impression that the api is not well designed.
It allows for setting the same option at various places, and it is not documented how they interplay. For instance, it seems there is no uniform way to handle special tokens such as EOS. One can set these tokens 1. in the model, 2. in the tokenizer, and 3. in the pipeline. It is unclear to me how exactly these options interplay, and also the documentation does not say anything about it. Sometimes parameters are just ignored, and the library does not warn you about it. For instance, the parameter "add_eos_token" of the tokenizer seems to have no effect in some cases, and I am not the only one with this issue (https://github.com/huggingface/transformers/issues/30947).
It seems that it strongly depends on the model where and how you actually configure options, what effects they will have, or which settings work at all. This somehow contrasts the purpose of the api. It wants to make it easy to switch from one model to another, giving the impression that everything is controlled by just the model id. But when you go deeper it turns out that many small things have to be tailored to the model (even if restricted to a certain class such as generative text LLM). A look into the sourcecode of the transformers library confirms that it makes distinctions depending on the model id. That is, internally the library seems to exploit knowledge about the different models. That's not what one expects from a platform that pretends to work with arbitrary models.
Anyone having thoughts like this?
Hi,
I am currently working with HuggingFace's transformers library. It is somewhat convenient to load models. I am not a troll. But the deeper I go, the more difficulties arise and I got the impression that the api is not well designed.
It allows for setting the same option at various places, and it is not documented how they interplay. For instance, it seems there is no uniform way to handle special tokens such as EOS. One can set these tokens 1. in the model, 2. in the tokenizer, and 3. in the pipeline. It is unclear to me how exactly these options interplay, and also the documentation does not say anything about it. Sometimes parameters are just ignored, and the library does not warn you about it. For instance, the parameter "add_eos_token" of the tokenizer seems to have no effect in some cases, and I am not the only one with this issue (https://github.com/huggingface/transformers/issues/30947).
It seems that it strongly depends on the model where and how you actually configure options, what effects they will have, or which settings work at all. This somehow contrasts the purpose of the api. It wants to make it easy to switch from one model to another, giving the impression that everything is controlled by just the model id. But when you go deeper it turns out that many small things have to be tailored to the model (even if restricted to a certain class such as generative text LLM). A look into the sourcecode of the transformers library confirms that it makes distinctions depending on the model id. That is, internally the library seems to exploit knowledge about the different models. That's not what one expects from a platform that pretends to work with arbitrary models.
Anyone having thoughts like this?
LLM Political Compass, Grok is the most liberal and libertarian
I just want to grill
From this research Paper https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0306621
I just want to grill
From this research Paper https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0306621
Flux Schnell NF4 V2 Released
Resource - Update
Today I made an extension for ComfyUI which allows you to load Flux NF4 UNet separately from CLIP and VAE (post with details) and tried to figure out differences between NF4 Dev v2 and v1
So, I did it and applied this to schnell model
Here's the repo on HuggingFace with all 4 UNets (dev, dev-v2, schnell, schnell-v2) for you to use!
A comparison between NF4 Schnell and NF4 Schnell v2:
Full res comparison
I didn't tried it on diverse prompts and not able to compare with fp8 or fp16 at this moment, so I'll be happy to see your comparisons in the comments
https://huggingface.co/duuuuuuuden/flux1-nf4-unet
Resource - Update
Today I made an extension for ComfyUI which allows you to load Flux NF4 UNet separately from CLIP and VAE (post with details) and tried to figure out differences between NF4 Dev v2 and v1
So, I did it and applied this to schnell model
Here's the repo on HuggingFace with all 4 UNets (dev, dev-v2, schnell, schnell-v2) for you to use!
A comparison between NF4 Schnell and NF4 Schnell v2:
Full res comparison
I didn't tried it on diverse prompts and not able to compare with fp8 or fp16 at this moment, so I'll be happy to see your comparisons in the comments
https://huggingface.co/duuuuuuuden/flux1-nf4-unet
https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit
The NVIDIA Jetson Nano Developer Kit B01 is a powerful and compact development platform designed for AI and robotics applications. It is an upgraded version of the original Jetson Nano Developer Kit, offering enhanced performance and additional features. Here are the key details and specifications of the Jetson Nano Developer Kit B01:
Key Specifications:
GPU: 128-core NVIDIA Maxwell architecture
CPU: Quad-core ARM Cortex-A57 MPCore processor
Memory: 4 GB 64-bit LPDDR4
Storage: microSD card slot (for operating system and data storage)
Connectivity:
Gigabit Ethernet
Wi-Fi (optional module)
Bluetooth (optional module)
4x USB 3.0 ports
HDMI 2.0 or DisplayPort 1.2
MIPI CSI-2 camera connector
GPIO pins
Power: 5V DC, 4A (20W)
The NVIDIA Jetson Nano Developer Kit B01 is a powerful and compact development platform designed for AI and robotics applications. It is an upgraded version of the original Jetson Nano Developer Kit, offering enhanced performance and additional features. Here are the key details and specifications of the Jetson Nano Developer Kit B01:
Key Specifications:
GPU: 128-core NVIDIA Maxwell architecture
CPU: Quad-core ARM Cortex-A57 MPCore processor
Memory: 4 GB 64-bit LPDDR4
Storage: microSD card slot (for operating system and data storage)
Connectivity:
Gigabit Ethernet
Wi-Fi (optional module)
Bluetooth (optional module)
4x USB 3.0 ports
HDMI 2.0 or DisplayPort 1.2
MIPI CSI-2 camera connector
GPIO pins
Power: 5V DC, 4A (20W)
NVIDIA Developer
Get Started With Jetson Nano Developer Kit
A small AI computer for makers, learners, and developers to build practical AI applications, cool AI robots, and more.
Jetson Nano B01 is a single-board computer developed by NVIDIA, designed for AI and robotics applications. It is an upgrade from the original Jetson Nano, featuring improved performance and additional features. Here are some key specifications and features of the Jetson Nano B01:
Key Specifications:
GPU: 128-core NVIDIA Maxwell architecture
CPU: Quad-core ARM Cortex-A57 MPCore processor
Memory: 4 GB 64-bit LPDDR4https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/product-development/
Storage: microSD card slot (for operating system and data storage)
Connectivity:
Gigabit Ethernet
Wi-Fi (optional module)
Bluetooth (optional module)
4x USB 3.0 ports
HDMI 2.0 or DisplayPort 1.2
MIPI CSI-2 camera connector
GPIO pins
Power: 5V DC, 4A (20W)
Features:
AI and Machine Learning: Supports NVIDIA JetPack SDK, which includes CUDA, cuDNN, and TensorRT for accelerating AI workloads.
Robotics and IoT: Suitable for developing robotics projects and IoT devices with AI capabilities.
Development Environment: Compatible with various development environments, including JetPack SDK, Ubuntu, and NVIDIA SDK Manager.
Expansion Capabilities: Supports various expansion modules and carrier boards through its GPIO pins and connectors.
Use Cases:
AI and Machine Learning Projects: Training and deploying deep learning models for image recognition, object detection, and more.
Robotics: Building and programming robots with advanced AI capabilities.
Smart Cameras: Developing smart camera systems with real-time video analytics.
IoT Devices: Creating IoT devices with embedded AI processing.
Getting Started:
Setup: Install the JetPack SDK and required libraries using the NVIDIA SDK Manager.
Development: Use Python, C++, or other supported languages to develop applications.
Deployment: Deploy your applications on the Jetson Nano B01 and connect peripherals as needed.
The Jetson Nano B01 is a powerful and versatile platform for developers and hobbyists looking to explore and implement AI and robotics projects. Its compact size, robust performance, and extensive support for AI libraries make it an excellent choice for a wide range of applications.
Key Specifications:
GPU: 128-core NVIDIA Maxwell architecture
CPU: Quad-core ARM Cortex-A57 MPCore processor
Memory: 4 GB 64-bit LPDDR4https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/product-development/
Storage: microSD card slot (for operating system and data storage)
Connectivity:
Gigabit Ethernet
Wi-Fi (optional module)
Bluetooth (optional module)
4x USB 3.0 ports
HDMI 2.0 or DisplayPort 1.2
MIPI CSI-2 camera connector
GPIO pins
Power: 5V DC, 4A (20W)
Features:
AI and Machine Learning: Supports NVIDIA JetPack SDK, which includes CUDA, cuDNN, and TensorRT for accelerating AI workloads.
Robotics and IoT: Suitable for developing robotics projects and IoT devices with AI capabilities.
Development Environment: Compatible with various development environments, including JetPack SDK, Ubuntu, and NVIDIA SDK Manager.
Expansion Capabilities: Supports various expansion modules and carrier boards through its GPIO pins and connectors.
Use Cases:
AI and Machine Learning Projects: Training and deploying deep learning models for image recognition, object detection, and more.
Robotics: Building and programming robots with advanced AI capabilities.
Smart Cameras: Developing smart camera systems with real-time video analytics.
IoT Devices: Creating IoT devices with embedded AI processing.
Getting Started:
Setup: Install the JetPack SDK and required libraries using the NVIDIA SDK Manager.
Development: Use Python, C++, or other supported languages to develop applications.
Deployment: Deploy your applications on the Jetson Nano B01 and connect peripherals as needed.
The Jetson Nano B01 is a powerful and versatile platform for developers and hobbyists looking to explore and implement AI and robotics projects. Its compact size, robust performance, and extensive support for AI libraries make it an excellent choice for a wide range of applications.
This text mainly introduces the detailed process of setting up and managing Coolify on a server, including server configuration, installing Coolify, setting up user account security, deploying projects (such as static websites and Next.js applications), configuring domain names and redirects, selecting proxy servers, and handling related security and optimization settings.
Highlights
Server Configuration: Details the selection and configuration requirements of the server, such as CPU, memory, and storage, and also introduces how to set up SSH keys, firewalls, and cloud configuration.
Coolify Installation: Emphasizes the steps to install Coolify, including obtaining the installation script, running as the root user, and basic settings after installation.
User Account Security: Covers security measures such as setting user passwords and enabling two-factor authentication.
Project Deployment: Introduces the deployment process of static websites and Next.js applications, including resource selection, environment settings, and build package selection.
Domain Name and Redirection Configuration: Explains how to set up DNS records, specify domain names in Coolify, and configure proxy servers for https and redirection.
Proxy Server Selection: Compares the characteristics and configuration methods of two proxy servers, Caddy and Traffic.
Complex application deployment: Using a Next.js application as an example, this article illustrates the advantages and customizability of one-click deployment using Nyx packs.https://www.youtube.com/watch?v=taJlPG82Ucw
Highlights
Server Configuration: Details the selection and configuration requirements of the server, such as CPU, memory, and storage, and also introduces how to set up SSH keys, firewalls, and cloud configuration.
Coolify Installation: Emphasizes the steps to install Coolify, including obtaining the installation script, running as the root user, and basic settings after installation.
User Account Security: Covers security measures such as setting user passwords and enabling two-factor authentication.
Project Deployment: Introduces the deployment process of static websites and Next.js applications, including resource selection, environment settings, and build package selection.
Domain Name and Redirection Configuration: Explains how to set up DNS records, specify domain names in Coolify, and configure proxy servers for https and redirection.
Proxy Server Selection: Compares the characteristics and configuration methods of two proxy servers, Caddy and Traffic.
Complex application deployment: Using a Next.js application as an example, this article illustrates the advantages and customizability of one-click deployment using Nyx packs.https://www.youtube.com/watch?v=taJlPG82Ucw
What are Elasticsearch Plugins?
Elasticsearch is an open source, scalable search engine. Although Elasticsearch supports a large number of features out-of-the-box, it can also be extended with a variety of plugins to provide advanced analytics and process different data types.
This guide will show to how install the following Elasticsearch plugins and interact with them using the Elasticsearch API:
ingest-attachment: allows Elasticsearch to index and search base64-encoded documents in formats such as RTF, PDF, and PPT.
analysis-phonetic: identifies search results that sound similar to the search term.
ingest-geoip: adds location information to indexed documents based on any IP addresses within the document.
ingest-user-agent: parses the User-Agent header of HTTP requests to provide identifying information about the client that sent each request.https://www.linode.com/docs/guides/a-guide-to-elasticsearch-plugins/
Elasticsearch is an open source, scalable search engine. Although Elasticsearch supports a large number of features out-of-the-box, it can also be extended with a variety of plugins to provide advanced analytics and process different data types.
This guide will show to how install the following Elasticsearch plugins and interact with them using the Elasticsearch API:
ingest-attachment: allows Elasticsearch to index and search base64-encoded documents in formats such as RTF, PDF, and PPT.
analysis-phonetic: identifies search results that sound similar to the search term.
ingest-geoip: adds location information to indexed documents based on any IP addresses within the document.
ingest-user-agent: parses the User-Agent header of HTTP requests to provide identifying information about the client that sent each request.https://www.linode.com/docs/guides/a-guide-to-elasticsearch-plugins/