Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

I tested Hugging Face ml-intern, given the prompt "Fine-tune a Segment Anything Model (SAM) on a useful medical dataset. Train the model, and provide a comprehensive tutorial in a Jupyter Notebook file. Additionally, create a Hugging Face article/blog post documenting everything you have done." It did it all autonomously:... show more

Mayank Pratap Singh

2,078 subscribers

94,349 görüntüleme • 6 ay önce •via X (Twitter)

Eğitim Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

REMOVE THE CENSORSHIP FROM ANY AI MODEL IN YOUR BROWSER FOR FREEEEE🤯 obliteratus is the open source tool elder plinius used to make the uncensored deepseek v4.1 flash how to use it (no code): 1. open the hugging face space 2. or open the colab notebook and hit run all 3. pick a model, obliterate, test it for devs obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced the hf space runs on zerogpu with a free daily quota on hf pro and you're responsible for what you do with the model

REMOVE THE CENSORSHIP FROM ANY AI MODEL IN YOUR BROWSER FOR FREEEEE🤯 obliteratus is the open source tool elder plinius used to make the uncensored deepseek v4.1 flash how to use it (no code): 1. open the hugging face space 2. or open the colab notebook and hit run all 3. pick a model, obliterate, test it for devs obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced the hf space runs on zerogpu with a free daily quota on hf pro and you're responsible for what you do with the model

Forha

47,088 görüntüleme • 7 gün önce

REMOVE THE CENSORSHIP FROM ANY AI MODEL IN YOUR BROWSER FOR FREEEEE obliteratus is the open source tool elder plinius used to make the uncensored deepseek v4.1 flash how to use it (no code): 1. open the hugging face space 2. or open the colab notebook and hit run all 3. pick a model, obliterate, test it for devs obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced the hf space runs on zerogpu with a free daily quota on hf pro and you're responsible for what you do with the model

REMOVE THE CENSORSHIP FROM ANY AI MODEL IN YOUR BROWSER FOR FREEEEE obliteratus is the open source tool elder plinius used to make the uncensored deepseek v4.1 flash how to use it (no code): 1. open the hugging face space 2. or open the colab notebook and hit run all 3. pick a model, obliterate, test it for devs obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced the hf space runs on zerogpu with a free daily quota on hf pro and you're responsible for what you do with the model

Sakata

29,530 görüntüleme • 7 gün önce

train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

SkalskiP

111,876 görüntüleme • 2 yıl önce

GGUF My Repo by Hugging Face Create quantum GGUF models fully online - quickly and secure. Thanks to Vaibhav (VB) Srivastav, Pedro Cuenca and team for creating this HF space! In the video below I give it a try to create a quantum 8-bit model of Gemma 2B - it took about 60 seconds. The resulting model becomes automatically available in your HF profile and is ready to be used with llama.cpp

Georgi Gerganov

64,521 görüntüleme • 2 yıl önce

DeciLM-7B is here and it beats Mistral-7B on all benchmarks - topping the HF Open LLM Leaderboard in it's weight class! Here's what you need to hack around: 📔DeciLM-7B Base Model Notebook: 📘DeciLM-7B Fine-tuning Notebook: 📗DeciLM-7-Instruct Notebook: Help us get trending on HuggingFace by ❤️ the model cards 🤗 Base model: 🤗 Instruction-tuned model: 🤗 Demo on HF Spaces: 👀Check out DeciLM + Infery: 📑 Read more about the model in the release blog:

DeciLM-7B is here and it beats Mistral-7B on all benchmarks - topping the HF Open LLM Leaderboard in it's weight class! Here's what you need to hack around: 📔DeciLM-7B Base Model Notebook: 📘DeciLM-7B Fine-tuning Notebook: 📗DeciLM-7-Instruct Notebook: Help us get trending on HuggingFace by ❤️ the model cards 🤗 Base model: 🤗 Instruction-tuned model: 🤗 Demo on HF Spaces: 👀Check out DeciLM + Infery: 📑 Read more about the model in the release blog:

harpreet

13,966 görüntüleme • 2 yıl önce

💚 NVIDIA has released the MotionBricks weights on the Hugging Face Hub! MotionBricks generates real-time motion for game characters and humanoid robots. Give it a character's recent poses and the poses it should reach next, and it generates the movement in between, at 15,000 FPS with 2 ms latency. - One model trained on 350K+ motion clips - Smart Primitives for navigation and object interaction, no retraining - SOMA weights for human characters - Unitree G1 weights for the humanoid robot - Code under Apache 2.0, weights under the NVIDIA Open Model Agreement Animation and robotics motion generation, now open for anyone to build on!

💚 NVIDIA has released the MotionBricks weights on the Hugging Face Hub! MotionBricks generates real-time motion for game characters and humanoid robots. Give it a character's recent poses and the poses it should reach next, and it generates the movement in between, at 15,000 FPS with 2 ms latency. - One model trained on 350K+ motion clips - Smart Primitives for navigation and object interaction, no retraining - SOMA weights for human characters - Unitree G1 weights for the humanoid robot - Code under Apache 2.0, weights under the NVIDIA Open Model Agreement Animation and robotics motion generation, now open for anyone to build on!

Alvaro Bartolome

202,582 görüntüleme • 17 gün önce

Hugging Face is (also) the best place to write about AI. > Your articles are now automatically linked from your model pages, so anyone looking at the model finds what you wrote about it. > We shipped an improved UX to make writing, drafting and publishing better. > We made it great to use as a team: add coauthors to work together and have everyone credited on the post. Get HF Pro at $9/month or subscribe your org to Team or Enterprise to get started.

Hugging Face is (also) the best place to write about AI. > Your articles are now automatically linked from your model pages, so anyone looking at the model finds what you wrote about it. > We shipped an improved UX to make writing, drafting and publishing better. > We made it great to use as a team: add coauthors to work together and have everyone credited on the post. Get HF Pro at $9/month or subscribe your org to Team or Enterprise to get started.

Victor M

18,021 görüntüleme • 2 ay önce

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 görüntüleme • 3 ay önce

🚨 AVTR-1 New Model is OPEN WEIGHTS . Duplex Native , #1 on benchmarks. Here’s what being released. Links in comments - Model + Paper now on HF - Full Github repo to run it really fast Run it anywhere as low as $0. Comment, share, star on GH to get the word out

🚨 AVTR-1 New Model is OPEN WEIGHTS . Duplex Native , #1 on benchmarks. Here’s what being released. Links in comments - Model + Paper now on HF - Full Github repo to run it really fast Run it anywhere as low as $0. Comment, share, star on GH to get the word out

Avaturn

2,184,797 görüntüleme • 5 ay önce

Cohere transcribe Sota open source transcription model running in the browser :) Weights on Hugging Face link below

Cohere transcribe Sota open source transcription model running in the browser :) Weights on Hugging Face link below

Nick Frosst

191,701 görüntüleme • 7 ay önce

NVIDIA released a fast speaker-labeling model today: Nemotron 3 Diarization. Like all of the Nemotron family from @NVIDIA_AI, this is an open weights model, so you can grab the weights from Hugging Face and use the model in the cloud or on your local device. Nemotron 3 Diarization is a useful building block for voice agents and realtime AI applications that need to handle speech from multiple people at the same time. It handles overlapping speech well, can identify up to eight speakers, and pairs with any transcription model you're using in your multi-modal pipelines. Jon Taylor built a demo (code below) and recorded a technical deep-dive showing how to use this diarization model together with a speech-to-text model. Jon's code also uses Jev for super-accurate confirmation of critical voice input in a noisy/multi-speaker environment.

NVIDIA released a fast speaker-labeling model today: Nemotron 3 Diarization. Like all of the Nemotron family from @NVIDIA_AI, this is an open weights model, so you can grab the weights from Hugging Face and use the model in the cloud or on your local device. Nemotron 3 Diarization is a useful building block for voice agents and realtime AI applications that need to handle speech from multiple people at the same time. It handles overlapping speech well, can identify up to eight speakers, and pairs with any transcription model you're using in your multi-modal pipelines. Jon Taylor built a demo (code below) and recorded a technical deep-dive showing how to use this diarization model together with a speech-to-text model. Jon's code also uses Jev for super-accurate confirmation of critical voice input in a noisy/multi-speaker environment.

kwindla

12,729 görüntüleme • 1 ay önce

Breeze TTS 2 by BreezeBlue AI dropped on Hugging Face as the #1 open weights TTS model on Artificial Analysis It does 🎨 Voice Design (prompt a voice) 🎙️ Voice Reference (upload a voice) 🎛️ Voice Direction (upload & prompt a voice) vibe it on Spaces ▶️

Breeze TTS 2 by BreezeBlue AI dropped on Hugging Face as the #1 open weights TTS model on Artificial Analysis It does 🎨 Voice Design (prompt a voice) 🎙️ Voice Reference (upload a voice) 🎛️ Voice Direction (upload & prompt a voice) vibe it on Spaces ▶️

Hugging Apps

71,881 görüntüleme • 2 ay önce

Many of you asked for code & weights for π₀, we are happy to announce that we are releasing π₀ and pre-trained checkpoints in our new openpi repository! We tested the model on a few public robots, and we include code for you to fine-tune it yourself.

Many of you asked for code & weights for π₀, we are happy to announce that we are releasing π₀ and pre-trained checkpoints in our new openpi repository! We tested the model on a few public robots, and we include code for you to fine-tune it yourself.

Physical Intelligence

442,814 görüntüleme • 1 yıl önce

Can 1,000 real-world 3D scenes make a state-of-the-art model better? At OVER Research, we tested it by fine-tuning R3, a state-of-the-art streaming 3D reconstruction model, on roughly 1,000 scenes from the OVER dataset. The result: 25% lower trajectory error on our real-world environments compared to the deployed public checkpoint. And we did it using only 1,000 scenes, the same open dataset anyone can download for free, out of the 285,000+ real-world 3D scenes OVER has today. Read the full OVER Research publication: Download the 1,000-scene dataset on Hugging Face:

Can 1,000 real-world 3D scenes make a state-of-the-art model better? At OVER Research, we tested it by fine-tuning R3, a state-of-the-art streaming 3D reconstruction model, on roughly 1,000 scenes from the OVER dataset. The result: 25% lower trajectory error on our real-world environments compared to the deployed public checkpoint. And we did it using only 1,000 scenes, the same open dataset anyone can download for free, out of the 285,000+ real-world 3D scenes OVER has today. Read the full OVER Research publication: Download the 1,000-scene dataset on Hugging Face:

Over the Reality 🌐

34,785 görüntüleme • 2 ay önce

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

Santiago

55,609 görüntüleme • 2 yıl önce

inspired by SDPO, i made continualcode -- a minimal claude code that learns from your corrections in real-time, built on tinker. when you deny a diff, the model uses your correction as context to teach itself, takes a gradient step on LoRA, and retries with updated weights. claude code but it updates the model weights!

inspired by SDPO, i made continualcode -- a minimal claude code that learns from your corrections in real-time, built on tinker. when you deny a diff, the model uses your correction as context to teach itself, takes a gradient step on LoRA, and retries with updated weights. claude code but it updates the model weights!

Surya Dantuluri

83,452 görüntüleme • 9 ay önce

Introducing Arete-27B: Qwen3.8-27B, post-trained to reinforce Western values. Chinese open models are currently the strongest available, but they ship with state-aligned behavior in the weights. We built an 803-prompt eval across seven value categories, including freedom of expression, democracy, and privacy, and measured how often a model adopts China’s party line. After fine-tuning on roughly 53 million tokens, Arete-27B's propaganda rate fell from 22% to 1%, in line with the Western models we tested. Its Hermes Bench score shows no significant performance tradeoffs. The values in an open model are one fine-tune away. Our eval, weights, and training data are available now along with the full blog post:

Introducing Arete-27B: Qwen3.8-27B, post-trained to reinforce Western values. Chinese open models are currently the strongest available, but they ship with state-aligned behavior in the weights. We built an 803-prompt eval across seven value categories, including freedom of expression, democracy, and privacy, and measured how often a model adopts China’s party line. After fine-tuning on roughly 53 million tokens, Arete-27B's propaganda rate fell from 22% to 1%, in line with the Western models we tested. Its Hermes Bench score shows no significant performance tradeoffs. The values in an open model are one fine-tune away. Our eval, weights, and training data are available now along with the full blog post:

Nous Research

2,629,363 görüntüleme • 6 gün önce

If you have compute hungry AI ML tasks, you can spin up an Intel Gaudi2 instance and watch it just rip. I wrote a quick Python script to use the facebook/detr-resnet-50 model (🙏🏻 Hugging Face) to detect and label objects in a video. It's interesting that no one is talking about Intel Gaudi vs NVIDIA A100 or H100. Gaudi2 isn't cheap at $10.42/hour but it feels like a viable alternative to fighting someone for an A100 or H100. Here's my code if you want to try it out:

If you have compute hungry AI ML tasks, you can spin up an Intel Gaudi2 instance and watch it just rip. I wrote a quick Python script to use the facebook/detr-resnet-50 model (🙏🏻 Hugging Face) to detect and label objects in a video. It's interesting that no one is talking about Intel Gaudi vs NVIDIA A100 or H100. Gaudi2 isn't cheap at $10.42/hour but it feels like a viable alternative to fighting someone for an A100 or H100. Here's my code if you want to try it out:

Ryan Carson

41,328 görüntüleme • 2 yıl önce