Share and discover more about AI with social posts from the community.huggingface/OpenAi
illusion diffusion - a arborvitae Collection
Illusion Diffusion HQ 🌀
Generate stunning high quality illusion artwork with Stable Diffusion
Illusion Diffusion is back up with a safety checker! Because I have been asked, if you would like to support me, consider using deforum.studio
A space by AP Follow me on Twitter with big contributions from multimodalart
This project works by using Monster Labs QR Control Net. Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: MrUgleh for discovering the workflow :)https://huggingface.co/spaces/AP123/IllusionDiffusion
Illusion Diffusion HQ 🌀
Generate stunning high quality illusion artwork with Stable Diffusion
Illusion Diffusion is back up with a safety checker! Because I have been asked, if you would like to support me, consider using deforum.studio
A space by AP Follow me on Twitter with big contributions from multimodalart
This project works by using Monster Labs QR Control Net. Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: MrUgleh for discovering the workflow :)https://huggingface.co/spaces/AP123/IllusionDiffusion
huggingface.co
IllusionDiffusion - a Hugging Face Space by AP123
Generate stunning high quality illusion artwork
The page https://www.chatpdf.com/?via=saasinfopro is likely related to a tool or service that allows users to interact with PDF documents using chat functionality. It probably enables users to ask questions about the content of the PDF, and the tool would provide answers based on the information within the document. This could be useful for quickly extracting information from large or complex PDFs without having to manually search through the entire document. However, without accessing the actual page, this is a general assumption based on the common functionality associated with such a domain name.
Here are the latest models I mentioned earlier,
LLaMA: A large language model developed by Meta, with excellent understanding and generation capabilities.
PaLM: Google's latest language model, with powerful natural language processing capabilities.
Claude: An AI chatbot developed by Anthropic, capable of engaging in natural language conversations.
DALL-E: A text-to-image model that can generate images based on text descriptions.
Stable Diffusion: A text-to-image model that can generate high-quality images.
GPT-4: OpenAI's latest language model, with powerful natural language processing capabilities.
Bard: Google's latest chatbot, capable of engaging in natural language conversations.
These models are all recent developments, with impressive capabilities and potential applications.
LLaMA: A large language model developed by Meta, with excellent understanding and generation capabilities.
PaLM: Google's latest language model, with powerful natural language processing capabilities.
Claude: An AI chatbot developed by Anthropic, capable of engaging in natural language conversations.
DALL-E: A text-to-image model that can generate images based on text descriptions.
Stable Diffusion: A text-to-image model that can generate high-quality images.
GPT-4: OpenAI's latest language model, with powerful natural language processing capabilities.
Bard: Google's latest chatbot, capable of engaging in natural language conversations.
These models are all recent developments, with impressive capabilities and potential applications.
Hugging Face ซื้อกิจการ XetHub สตาร์ทอัปสตอเรจสำหรับงาน AI
Hugging Face ประกาศซื้อกิจการ XetHub สตาร์ทอัปที่พัฒนาสตอเรจสำหรับงานแอพพลิเคชัน Machine Learning และ AI ซึ่งสามารถรองรับความต้องการของ Hugging Face ได้
Yucheng Low ซีอีโอ XetHub กล่าวว่าวิสัยทัศน์ของ Hugging Face คือการนำโมเดล AI มาเผยแพร่ให้กับทุกคน ซึ่งต้องการพื้นที่รองรับการเก็บข้อมูลและการเข้าถึง การที่ XetHub เข้ามาเป็นส่วนหนึ่งของ Hugging Face จึงช่วยเติมเต็มวิสัยทัศน์นี้สำหรับอนาคตของ AI ได้ ส่วน Julien Chaumond ซีทีโอ Hugging Face บอกว่าความท้าทายจากนี้คือขนาด Repository ที่ใหญ่มากขึ้น ปัจจุบัน Hugging Face มี Repo ถึง 1.3 ล้านโมเดล, ข้อมูล 450k datasets, รีเควสวันละ 1B และแบนด์วิธที่ Cloudfront วันละ 6PB
Hugging Face ไม่ได้เปิดเผยมูลค่าของดีลดังกล่าว แต่บอกว่าพนักงาน XetHub ทั้ง 14 คน จะเข้ามาร่วมงานกับ Hugging Face
XetHub ก่อตั้งในปี 2021 โดย Yucheng Low และ Rajat Arya ซึ่งทั้งสองคนเคยทำงานที่ Turi สตาร์ทอัปด้าน AI ที่แอปเปิลซื้อกิจการในปี 2016 ต่อมาทั้งสองคนลาออกจากแอปเปิลและมาก่อตั้ง XetHub
ที่มา: GeekWire
Hugging Face ประกาศซื้อกิจการ XetHub สตาร์ทอัปที่พัฒนาสตอเรจสำหรับงานแอพพลิเคชัน Machine Learning และ AI ซึ่งสามารถรองรับความต้องการของ Hugging Face ได้
Yucheng Low ซีอีโอ XetHub กล่าวว่าวิสัยทัศน์ของ Hugging Face คือการนำโมเดล AI มาเผยแพร่ให้กับทุกคน ซึ่งต้องการพื้นที่รองรับการเก็บข้อมูลและการเข้าถึง การที่ XetHub เข้ามาเป็นส่วนหนึ่งของ Hugging Face จึงช่วยเติมเต็มวิสัยทัศน์นี้สำหรับอนาคตของ AI ได้ ส่วน Julien Chaumond ซีทีโอ Hugging Face บอกว่าความท้าทายจากนี้คือขนาด Repository ที่ใหญ่มากขึ้น ปัจจุบัน Hugging Face มี Repo ถึง 1.3 ล้านโมเดล, ข้อมูล 450k datasets, รีเควสวันละ 1B และแบนด์วิธที่ Cloudfront วันละ 6PB
Hugging Face ไม่ได้เปิดเผยมูลค่าของดีลดังกล่าว แต่บอกว่าพนักงาน XetHub ทั้ง 14 คน จะเข้ามาร่วมงานกับ Hugging Face
XetHub ก่อตั้งในปี 2021 โดย Yucheng Low และ Rajat Arya ซึ่งทั้งสองคนเคยทำงานที่ Turi สตาร์ทอัปด้าน AI ที่แอปเปิลซื้อกิจการในปี 2016 ต่อมาทั้งสองคนลาออกจากแอปเปิลและมาก่อตั้ง XetHub
ที่มา: GeekWire
GOOD Hugging Face buys AI file management platform XetHub
Hugging Face recently acquired XetHub. With this, the hosting platform for LLMs is buying a start-up that helps developers manage the files they need to run AI projects.
The exact amount of the acquisition was not disclosed, but it is said to be Hugging Face’s largest to date. Among other things, this means that the acquisition amount is higher than the $10 million the LLM hosting platform paid for AI training specialist Argilla in June this year.
Code file management platform
With XetHub, Hugging Face gets its hands on a start-up that provides a platform for developers to store the code files and other technical solutions they use for AI projects. The main focus here is machine learning.
In addition, this platform offers several other tools to manage the ML stack. These include version control, keeping an eye on assets, making changes in a single place and more. The platform also offers (collaboration) productivity tools that should make it easier for developers to work with these specific files, edit them, improve performance and make better comparisons with other tooling.https://www.techzine.eu/news/applications/123327/hugging-face-buys-ai-file-management-platform-xethub/
Hugging Face recently acquired XetHub. With this, the hosting platform for LLMs is buying a start-up that helps developers manage the files they need to run AI projects.
The exact amount of the acquisition was not disclosed, but it is said to be Hugging Face’s largest to date. Among other things, this means that the acquisition amount is higher than the $10 million the LLM hosting platform paid for AI training specialist Argilla in June this year.
Code file management platform
With XetHub, Hugging Face gets its hands on a start-up that provides a platform for developers to store the code files and other technical solutions they use for AI projects. The main focus here is machine learning.
In addition, this platform offers several other tools to manage the ML stack. These include version control, keeping an eye on assets, making changes in a single place and more. The platform also offers (collaboration) productivity tools that should make it easier for developers to work with these specific files, edit them, improve performance and make better comparisons with other tooling.https://www.techzine.eu/news/applications/123327/hugging-face-buys-ai-file-management-platform-xethub/
Techzine Global
Hugging Face buys AI file management platform XetHub
Hugging Face recently acquired XetHub. With this, the hosting platform for LLMs is buying a start-up that helps developers manage the files they need to
What is the best LLM for RAG systems? 🤔
In a business setting, it will be the one that gives the best performance at a great price! 💼💰
And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨
That's @Google Gemini 1.5 Flash! 🚀🌟
It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖
But is it any good? 🤷♂️
On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. 📊🎯
Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌
Happy tuning... 🎶🔧
Gemini 1.5 Flash: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/ 🔗
LLM Hallucination Index: https://www.rungalileo.io/hallucinationindex 🔗
So the base model must be expensive? 💸
For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. 📉💵
In a business setting, it will be the one that gives the best performance at a great price! 💼💰
And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨
That's @Google Gemini 1.5 Flash! 🚀🌟
It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖
But is it any good? 🤷♂️
On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. 📊🎯
Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌
Happy tuning... 🎶🔧
Gemini 1.5 Flash: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/ 🔗
LLM Hallucination Index: https://www.rungalileo.io/hallucinationindex 🔗
So the base model must be expensive? 💸
For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. 📉💵
Googleblog
Google for Developers Blog - News about Web, Mobile, AI and Cloud
Gemini 1.5 Flash is now available to developers at more than 70% lower prices. Set up billing for Gemini API in Google AI Studio and access other new features like 1.5 Flash tuning.
Check Your Redirects and Status Codes
Quickly analyze 301, 302 redirects and other HTTP status codes to optimize your website's performance and SEO.
https://redirect-checker.girff.com/
Redirect Checker - Analyze Website Redirects and HTTP Status Codes
Use our free Redirect Checker tool to analyze your website's redirects, HTTP status codes, and headers. Optimize your SEO and improve user experience.
Quickly analyze 301, 302 redirects and other HTTP status codes to optimize your website's performance and SEO.
https://redirect-checker.girff.com/
Redirect Checker - Analyze Website Redirects and HTTP Status Codes
Use our free Redirect Checker tool to analyze your website's redirects, HTTP status codes, and headers. Optimize your SEO and improve user experience.
A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone
MiniCPM-V 2.6
MiniCPM-V 2.6 is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-Llama3-V 2.5, and introduces new features for multi-image and video understanding. Notable features of MiniCPM-V 2.6 include:
🔥 Leading Performance. MiniCPM-V 2.6 achieves an average score of 65.2 on the latest version of OpenCompass, a comprehensive evaluation over 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o mini, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet for single image understanding.
🖼 Multi Image Understanding and In-context Learning. MiniCPM-V 2.6 can also perform conversation and reasoning over multiple images. It achieves state-of-the-art performance on popular multi-image benchmarks such as Mantis-Eval, BLINK, Mathverse mv and Sciverse mv, and also shows promising in-context learning capability.
🎬 Video Understanding. MiniCPM-V 2.6 can also accept video inputs, performing conversation and providing dense captions for spatial-temporal information. It outperforms GPT-4V, Claude 3.5 Sonnet and LLaVA-NeXT-Video-34B on Video-MME with/without subtitles.
💪 Strong OCR Capability and Others. MiniCPM-V 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves state-of-the-art performance on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V, and Gemini 1.5 Pro. Based on the the latest RLAIF-V and VisCPM techniques, it features trustworthy behaviors, with significantly lower hallucination rates than GPT-4o and GPT-4V on Object HalBench, and supports multilingual capabilities on English, Chinese, German, French, Italian, Korean, etc.
🚀 Superior Efficiency. In addition to its friendly size, MiniCPM-V 2.6 also shows state-of-the-art token density (i.e., number of pixels encoded into each visual token). It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support real-time video understanding on end-side devices such as iPad.
💫 Easy Usage. MiniCPM-V 2.6 can be easily used in various ways: (1) llama.cpp and ollama support for efficient CPU inference on local devices, (2) int4 and GGUF format quantized models in 16 sizes, (3) vLLM support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with Gradio and (6) online web demo.https://huggingface.co/openbmb/MiniCPM-V-2_6
MiniCPM-V 2.6
MiniCPM-V 2.6 is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-Llama3-V 2.5, and introduces new features for multi-image and video understanding. Notable features of MiniCPM-V 2.6 include:
🔥 Leading Performance. MiniCPM-V 2.6 achieves an average score of 65.2 on the latest version of OpenCompass, a comprehensive evaluation over 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o mini, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet for single image understanding.
🖼 Multi Image Understanding and In-context Learning. MiniCPM-V 2.6 can also perform conversation and reasoning over multiple images. It achieves state-of-the-art performance on popular multi-image benchmarks such as Mantis-Eval, BLINK, Mathverse mv and Sciverse mv, and also shows promising in-context learning capability.
🎬 Video Understanding. MiniCPM-V 2.6 can also accept video inputs, performing conversation and providing dense captions for spatial-temporal information. It outperforms GPT-4V, Claude 3.5 Sonnet and LLaVA-NeXT-Video-34B on Video-MME with/without subtitles.
💪 Strong OCR Capability and Others. MiniCPM-V 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves state-of-the-art performance on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V, and Gemini 1.5 Pro. Based on the the latest RLAIF-V and VisCPM techniques, it features trustworthy behaviors, with significantly lower hallucination rates than GPT-4o and GPT-4V on Object HalBench, and supports multilingual capabilities on English, Chinese, German, French, Italian, Korean, etc.
🚀 Superior Efficiency. In addition to its friendly size, MiniCPM-V 2.6 also shows state-of-the-art token density (i.e., number of pixels encoded into each visual token). It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support real-time video understanding on end-side devices such as iPad.
💫 Easy Usage. MiniCPM-V 2.6 can be easily used in various ways: (1) llama.cpp and ollama support for efficient CPU inference on local devices, (2) int4 and GGUF format quantized models in 16 sizes, (3) vLLM support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with Gradio and (6) online web demo.https://huggingface.co/openbmb/MiniCPM-V-2_6
huggingface.co
openbmb/MiniCPM-V-2_6 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Kling AI Video is FINALLY Public (All Countries), Free to Use and MIND BLOWING - Full Tutorial > https://youtu.be/zcpqAxYV1_w
You probably seen those mind blowing AI made videos. And the day has arrived. The famous Kling AI is now worldwide available for free. In this tutorial video I will show you how to register for free with just email to Kling AI and use its mind blowing text to video animation, image to video animation and text to image, and image to image capabilities. This video will show you non-cherry pick results so you will know the actual quality and capability of the model unlike those extremely cherry pick example demos. Still, #KlingAI is the only #AI model that competes with OpenAI's #SORA and it is real to use.
🔗 Kling AI Official Website ⤵️
▶️ https://www.klingai.com/
🔗 Our GitHub Repository ⤵️
▶️ https://github.com/FurkanGozukara/Stable-Diffusion
You probably seen those mind blowing AI made videos. And the day has arrived. The famous Kling AI is now worldwide available for free. In this tutorial video I will show you how to register for free with just email to Kling AI and use its mind blowing text to video animation, image to video animation and text to image, and image to image capabilities. This video will show you non-cherry pick results so you will know the actual quality and capability of the model unlike those extremely cherry pick example demos. Still, #KlingAI is the only #AI model that competes with OpenAI's #SORA and it is real to use.
🔗 Kling AI Official Website ⤵️
▶️ https://www.klingai.com/
🔗 Our GitHub Repository ⤵️
▶️ https://github.com/FurkanGozukara/Stable-Diffusion
I just had a masterclass in open-source collaboration with the release of Llama 3.1 🦙🤗
Meta dropped Llama 3.1, and seeing firsthand the Hugging Face team working to integrate it is nothing short of impressive. Their swift integration, comprehensive documentation, and innovative tools showcase the power of open-source teamwork.
For the curious minds:
📊 Check out independent evaluations:
open-llm-leaderboard/open_llm_leaderboard
🧠 Deep dive into the tech: https://huggingface.co/blog/llama31
👨🍳 Try different recipes (including running 8B on free Colab!): https://github.com/huggingface/huggingface-llama-recipes
📈 Visualize open vs. closed LLM progress:
andrewrreed/closed-vs-open-arena-elo
🤖 Generate synthetic data with distilabel, thanks to the new license allowing the use of outputs to train other LLMs https://huggingface.co/blog/llama31#synthetic-data-generation-with-distilabel
💡 Pro tip: Experience the 405B version for free on HuggingChat, now with tool-calling capabilities! https://huggingface.co/chat/
#OpenSourceAI #AIInnovation
Meta dropped Llama 3.1, and seeing firsthand the Hugging Face team working to integrate it is nothing short of impressive. Their swift integration, comprehensive documentation, and innovative tools showcase the power of open-source teamwork.
For the curious minds:
📊 Check out independent evaluations:
open-llm-leaderboard/open_llm_leaderboard
🧠 Deep dive into the tech: https://huggingface.co/blog/llama31
👨🍳 Try different recipes (including running 8B on free Colab!): https://github.com/huggingface/huggingface-llama-recipes
📈 Visualize open vs. closed LLM progress:
andrewrreed/closed-vs-open-arena-elo
🤖 Generate synthetic data with distilabel, thanks to the new license allowing the use of outputs to train other LLMs https://huggingface.co/blog/llama31#synthetic-data-generation-with-distilabel
💡 Pro tip: Experience the 405B version for free on HuggingChat, now with tool-calling capabilities! https://huggingface.co/chat/
#OpenSourceAI #AIInnovation
huggingface.co
Llama 3.1 - 405B, 70B & 8B with multilinguality and long context
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
📚 Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset:
Omartificial-Intelligence-Space/Arabic-NLi-Triplet
6️⃣ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.
🪆 Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.
📈 Outperforms all commonly used multilingual models like
intfloat/multilingual-e5-large
,
sentence-transformers/paraphrase-multilingual-mpnet-base-v2
, and
sentence-transformers/LaBSE
.
Check them out here:
-
Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
-
Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka
-
Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
-
Omartificial-Intelligence-Space/Arabic-labse-Matryoshka
-
Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka
-
Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet
Or the collection with all:
Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e
My personal favourite is likely
Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
: a very efficient 135M parameters & scores #1 on
mteb/leaderboard
https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet
.
Omartificial-Intelligence-Space/Arabic-NLi-Triplet
6️⃣ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.
🪆 Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.
📈 Outperforms all commonly used multilingual models like
intfloat/multilingual-e5-large
,
sentence-transformers/paraphrase-multilingual-mpnet-base-v2
, and
sentence-transformers/LaBSE
.
Check them out here:
-
Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
-
Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka
-
Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
-
Omartificial-Intelligence-Space/Arabic-labse-Matryoshka
-
Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka
-
Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet
Or the collection with all:
Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e
My personal favourite is likely
Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
: a very efficient 135M parameters & scores #1 on
mteb/leaderboard
https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet
.
huggingface.co
Omartificial-Intelligence-Space/Arabic-NLi-Triplet · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 🐶
Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook 📖
Fine-tuning notebook: https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb
Resulting model:
merve/idefics3llama-vqav2
Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook 📖
Fine-tuning notebook: https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb
Resulting model:
merve/idefics3llama-vqav2
New feature 🔥
Image models and LoRAs now have little previews 🤏
If you don't know where to start to find them, I invite you to browse cool LoRAs in the profile of some amazing fine-tuners: @artificialguybr, @alvdansen, @DoctorDiffusion, @e-n-v-y, @KappaNeuro @ostris
Image models and LoRAs now have little previews 🤏
If you don't know where to start to find them, I invite you to browse cool LoRAs in the profile of some amazing fine-tuners: @artificialguybr, @alvdansen, @DoctorDiffusion, @e-n-v-y, @KappaNeuro @ostris
The first open Stable Diffusion 3-like architecture model is JUST out 💣 - but it is not SD3! 🤔
It is
Tencent-Hunyuan/HunyuanDiT
by Tencent, a 1.5B parameter DiT (diffusion transformer) text-to-image model 🖼✨, trained with multi-lingual CLIP + multi-lingual T5 text-encoders for english 🤝 chinese understanding
Try it out by yourself here ▶️ https://huggingface.co/spaces/multimodalart/HunyuanDiT
(a bit too slow as the model is chunky and the research code isn't super optimized for inference speed yet)
In the paper they claim to be SOTA open source based on human preference evaluation!
It is
Tencent-Hunyuan/HunyuanDiT
by Tencent, a 1.5B parameter DiT (diffusion transformer) text-to-image model 🖼✨, trained with multi-lingual CLIP + multi-lingual T5 text-encoders for english 🤝 chinese understanding
Try it out by yourself here ▶️ https://huggingface.co/spaces/multimodalart/HunyuanDiT
(a bit too slow as the model is chunky and the research code isn't super optimized for inference speed yet)
In the paper they claim to be SOTA open source based on human preference evaluation!
huggingface.co
HunyuanDiT - a Hugging Face Space by Tencent-Hunyuan
Discover amazing ML apps made by the community
🥳celebrating 5K readers in one of my blog posts 🥳
I came back with another one this time 🤓
in this blog you will learn 📖 :
* How to train custom AI models with the trainer API 🚀
* integrate your AI models with HF using the mixin classes 🔥
happy reading everyone 🤗
🔗link: https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes
I came back with another one this time 🤓
in this blog you will learn 📖 :
* How to train custom AI models with the trainer API 🚀
* integrate your AI models with HF using the mixin classes 🔥
happy reading everyone 🤗
🔗link: https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes
huggingface.co
Train custom AI models with the trainer API and adapt them to 🤗
A Blog post by Lain on Hugging Face
I will be delivering an introductory coding session this Sunday 7Pm gmt+1 time about huggingface, if you are new to HF and don't know where to begin, you are welcome to join us 🤗
📌Place: huggingface discord server
🔗Link : https://discord.gg/hugging-face-879548962464493619?event=1245406127668203541
📌Place: huggingface discord server
🔗Link : https://discord.gg/hugging-face-879548962464493619?event=1245406127668203541
Discord
Join the Hugging Face Discord Server!
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