Running
75
🚀
AutoTrain Advanced
Create powerful AI models without code
AutoTrain Projects
AI & ML interests
Official projects from Hugging Face AutoTrain
Recent Activity
View all activity
autotrain-projects's activity
abhishek
updated
a
Space
20 days ago
Post
1855
🎉 SUPER BLACK FRIDAY DEAL 🎉
Train almost any model on a variety of tasks such as llm finetuning, text classification/regression, summarization, question answering, image classification/regression, object detection, tabular data, etc for FREE using AutoTrain locally. 🔥
https://github.com/huggingface/autotrain-advanced
Train almost any model on a variety of tasks such as llm finetuning, text classification/regression, summarization, question answering, image classification/regression, object detection, tabular data, etc for FREE using AutoTrain locally. 🔥
https://github.com/huggingface/autotrain-advanced
Post
5689
INTRODUCING Hugging Face AutoTrain Client 🔥
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks 🤗
To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.
"pip install autotrain-advanced"
Github repo: https://github.com/huggingface/autotrain-advanced
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks 🤗
To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.
"pip install autotrain-advanced"
Github repo: https://github.com/huggingface/autotrain-advanced
-
- 6 replies
abhishek
authored
a
paper
3 months ago
multimodalart
updated
a
Space
3 months ago
Post
1859
🚨 NEW TASK ALERT 🚨
Extractive Question Answering: because sometimes generative is not all you need 😉
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 🚀
Check out the blog post on getting started with this task: https://huggingface.co/blog/abhishek/extractive-qa-autotrain
Extractive Question Answering: because sometimes generative is not all you need 😉
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 🚀
Check out the blog post on getting started with this task: https://huggingface.co/blog/abhishek/extractive-qa-autotrain
multimodalart
posted
an
update
6 months ago
Post
21656
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
- 2 replies
Post
3320
You can now train/finetune custom sentence transformer embedding models using AutoTrain. Read blog: https://huggingface.co/blog/abhishek/finetune-custom-embeddings-autotrain
-
- 2 replies
abhishek
updated
a
Space
8 months ago
Post
2938
🚨 NEW TASK ALERT 🚨
🎉 AutoTrain now supports Object Detection! 🎉
Transform your projects with these powerful new features:
🔹 Fine-tune any supported model from the Hugging Face Hub
🔹 Seamless logging with TensorBoard or W&B
🔹 Support for local and hub datasets
🔹 Configurable training for tailored results
🔹 Train locally or leverage Hugging Face Spaces
🔹 Deployment-ready with API inference or Hugging Face endpoints
AutoTrain: https://hf.co/autotrain
🎉 AutoTrain now supports Object Detection! 🎉
Transform your projects with these powerful new features:
🔹 Fine-tune any supported model from the Hugging Face Hub
🔹 Seamless logging with TensorBoard or W&B
🔹 Support for local and hub datasets
🔹 Configurable training for tailored results
🔹 Train locally or leverage Hugging Face Spaces
🔹 Deployment-ready with API inference or Hugging Face endpoints
AutoTrain: https://hf.co/autotrain
multimodalart
posted
an
update
8 months ago
Post
26983
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!
Post
3066
🚀🚀🚀🚀 Introducing AutoTrain Configs! 🚀🚀🚀🚀
Now you can train models using yaml config files! 💥 These configs are easy to understand and are not at all overwhelming. So, even a person with almost zero knowledge of machine learning can train state of the art models without writing any code. Check out example configs in the config directory of autotrain-advanced github repo and feel free to share configs by creating a pull request 🤗
Github repo: https://github.com/huggingface/autotrain-advanced
Now you can train models using yaml config files! 💥 These configs are easy to understand and are not at all overwhelming. So, even a person with almost zero knowledge of machine learning can train state of the art models without writing any code. Check out example configs in the config directory of autotrain-advanced github repo and feel free to share configs by creating a pull request 🤗
Github repo: https://github.com/huggingface/autotrain-advanced
- 2 replies
Post
3073
Post
2372
Trained another version of llama3-8b-instruct which beats the base model. This time without losing too many points on gsm8k benchmark. Again, using AutoTrain 💥 pip install autotrain-advanced
Trained model: abhishek/autotrain-llama3-orpo-v2
Trained model: abhishek/autotrain-llama3-orpo-v2
-
- 1 reply
Post
3478
With AutoTrain, you can already finetune the latest llama3 models without writing a single line of code. Here's an example finetune of llama3 8b model:
abhishek/autotrain-llama3-no-robots
-
- 2 replies
multimodalart
posted
an
update
11 months ago
Post
The Stable Diffusion 3 research paper broken down, including some overlooked details! 📝
Model
📏 2 base model variants mentioned: 2B and 8B sizes
📐 New architecture in all abstraction levels:
- 🔽 UNet; ⬆️ Multimodal Diffusion Transformer, bye cross attention 👋
- 🆕 Rectified flows for the diffusion process
- 🧩 Still a Latent Diffusion Model
📄 3 text-encoders: 2 CLIPs, one T5-XXL; plug-and-play: removing the larger one maintains competitiveness
🗃️ Dataset was deduplicated with SSCD which helped with memorization (no more details about the dataset tho)
Variants
🔁 A DPO fine-tuned model showed great improvement in prompt understanding and aesthetics
✏️ An Instruct Edit 2B model was trained, and learned how to do text-replacement
Results
✅ State of the art in automated evals for composition and prompt understanding
✅ Best win rate in human preference evaluation for prompt understanding, aesthetics and typography (missing some details on how many participants and the design of the experiment)
Paper: https://stabilityai-public-packages.s3.us-west-2.amazonaws.com/Stable+Diffusion+3+Paper.pdf
Model
📏 2 base model variants mentioned: 2B and 8B sizes
📐 New architecture in all abstraction levels:
- 🔽 UNet; ⬆️ Multimodal Diffusion Transformer, bye cross attention 👋
- 🆕 Rectified flows for the diffusion process
- 🧩 Still a Latent Diffusion Model
📄 3 text-encoders: 2 CLIPs, one T5-XXL; plug-and-play: removing the larger one maintains competitiveness
🗃️ Dataset was deduplicated with SSCD which helped with memorization (no more details about the dataset tho)
Variants
🔁 A DPO fine-tuned model showed great improvement in prompt understanding and aesthetics
✏️ An Instruct Edit 2B model was trained, and learned how to do text-replacement
Results
✅ State of the art in automated evals for composition and prompt understanding
✅ Best win rate in human preference evaluation for prompt understanding, aesthetics and typography (missing some details on how many participants and the design of the experiment)
Paper: https://stabilityai-public-packages.s3.us-west-2.amazonaws.com/Stable+Diffusion+3+Paper.pdf
- 3 replies
multimodalart
posted
an
update
11 months ago
Post
⚔️ The TIGERLab's Text2Image arena is here! ⚔️
TIGER-Lab/GenAI-Arena
Like https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard for LLMs: you prompt, two images emerge, vote for the best one 🏆
With enough votes this will lead to an Elo-based leaderboard for text-to-image models, go vote! 🗳️
TIGER-Lab/GenAI-Arena
TIGER-Lab/GenAI-Arena
Like https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard for LLMs: you prompt, two images emerge, vote for the best one 🏆
With enough votes this will lead to an Elo-based leaderboard for text-to-image models, go vote! 🗳️
TIGER-Lab/GenAI-Arena
multimodalart
posted
an
update
12 months ago
Post
It seems February started with a fully open source AI renaissance 🌟
Models released with fully open dataset, training code, weights ✅
LLM - allenai/olmo-suite-65aeaae8fe5b6b2122b46778 🧠
Embedding - nomic-ai/nomic-embed-text-v1 📚 (sota!)
And it's literally February 1st - can't wait to see what else the community will bring 👀
Models released with fully open dataset, training code, weights ✅
LLM - allenai/olmo-suite-65aeaae8fe5b6b2122b46778 🧠
Embedding - nomic-ai/nomic-embed-text-v1 📚 (sota!)
And it's literally February 1st - can't wait to see what else the community will bring 👀