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] | Minimalistic Adapters ๐
๐Demo Here:
https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
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{ Quote Tuner } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Quote-LoRA
{ Stamp Art } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Stamp-Art-LoRA
{ Hand Sticky } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Hand-Sticky-LoRA
{ Poster HQ } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Poster-HQ-LoRA
{ Ctoon Min } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Ctoon-LoRA
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{ LoRA Space Collection } : https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32
๐For More Visit
https://huggingface.co/strangerzonehf
.
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๐ค@prithivMLmods
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] | Kohya brought massive improvements to FLUX LoRA (as low as 4 GB GPUs) and DreamBooth / Fine-Tuning (as low as 6 GB GPUs) training - check attached images in full size to see full details
You can download all configs and full instructions
> https://www.patreon.com/posts/112099700 - Fine Tuning post
> https://www.patreon.com/posts/110879657 - LoRA post
Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training.
Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training
You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing.
You can download all configs and full instructions > https://www.patreon.com/posts/112099700
The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute
The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute
You can read the recent updates here : https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates
This is the Kohya GUI branch : https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1
Key thing to reduce VRAM usage is using block swap
Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well
Now you can do FP16 training with LoRAs on 24 GB and below GPUs
Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training)
It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :) | {
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Mediapipe 68-points Eyes-Closed and Mouth-Opened
https://huggingface.co/spaces/Akjava/mediapipe-68-facial-guide-eyes-closed-mouth-opened
[Article]Results: Converted Guide Images(eyes-closed and mouth-opened) with Flux.1 schenll img2img/inpaint
https://huggingface.co/blog/Akjava/result-guide-image-eyes-mouth
All the other tools listed are designed to support Mediapipe Face Animation
https://huggingface.co/collections/Akjava/mediapipe-tools-672ffe8ee7b62763c31b70c7
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This innovative framework enables the direct generation of 3D meshes from natural language prompts while maintaining strong language capabilities.
Here is the Architecture & Implementation!
>> Core Components
Model Foundation
- If you haven't guessed it yet, it's built on the LLaMA-3.1-8B-Instruct base model
- Maintains original language capabilities while adding 3D generation
- Context length is set to 8,000 tokens
3D Representation Strategy
- Uses the OBJ file format for mesh representation
- Quantizes vertex coordinates into 64 discrete bins per axis
- Sorts vertices by z-y-x coordinates, from lowest to highest
- Sorts faces by the lowest vertex indices for consistency
Data Processing Pipeline
- Filters meshes to a maximum of 500 faces for computational efficiency
- Applies random rotations (0ยฐ, 90ยฐ, 180ยฐ, 270ยฐ) for data augmentation
- Generates ~125k mesh variations from 31k base meshes
- Uses Cap3D-generated captions for text descriptions
>> Training Framework
Dataset Composition
- 40% Mesh Generation tasks
- 20% Mesh Understanding tasks
- 40% General Conversation (UltraChat dataset)
- 8x training turns for generation, 4x for understanding
Training Configuration
- Deployed on 32 A100 GPUs (for Nvidia, this is literally in-house)
- 21,000 training iterations
- Global batch size: 128
- AdamW optimizer with a 1e-5 learning rate
- 30-step warmup with cosine scheduling
- Total training time: approximately 3 days (based on the paper)
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๐จ a new vision language model with 9x less image tokens, super efficient
๐ aligned with DPO for reducing hallucinations
โก๏ธ Apache 2.0 license ๐ฅ
Demo hf.co/spaces/NexaAIDev/omnivlm-dpo-demo
Model https://huggingface.co/NexaAIDev/omnivision-968M | {
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https://pypi.org/project/llm-forwarder/
More details
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You can access our platform here at https://mann-e.com. We're still working on it and reducing the bugs and we also are trying to add a guest session which lets you make images as guests.
What do you think? | {
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] | It's not every day you see the No. 1 ranked paper of the day open-sourcing a very powerful image editing app!
Fascinating to see MagicQuill - a groundbreaking interactive image editing system that makes precise photo editing effortless through advanced AI!
The system's architecture features three sophisticated components:
1. Editing Processor:
- Implements a dual-branch architecture integrated into a latent diffusion framework
- Utilizes PiDiNet for edge map extraction and content-aware per-pixel inpainting
- Features a specialized UNet architecture with zero-convolution layers for feature insertion
- Employs denoising score matching for training the control branch
- Processes both structural modifications via scribble guidance and color manipulation through downsampled color blocks
- Maintains pixel-level control through VAE-based latent space operations
2. Painting Assistor:
- Powered by a fine-tuned LLaVA multimodal LLM using Low-Rank Adaptation (LoRA)
- Trained on a custom dataset derived from Densely Captioned Images (DCI)
- Processes user brushstrokes through specialized Q&A tasks for add/subtract/color operations
- Features bounding box coordinate normalization for precise stroke localization
- Implements streamlined single-word/phrase outputs for real-time performance
3. Idea Collector:
- Built as a modular ReactJS component library
- Supports cross-platform deployment via HTTP protocols
- Compatible with Gradio and ComfyUI frameworks
- Features comprehensive layer management and parameter adjustment capabilities
- Implements real-time canvas updates and preview generation
The system outperforms existing solutions like SmartEdit and BrushNet in edge alignment and color fidelity while maintaining seamless integration with popular AI frameworks.
What are your thoughts on AI-powered creative tools? | {
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] | ๐๏ธHope๐๏ธ and โ๏ธJusticeโ๏ธ AI
๐ฒ Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail.
๐ฌ So I Created an AI+Art+Music tribute:
-๐ง AI App that Evaluates GPT-4o vs Claude:
https://huggingface.co/spaces/awacke1/RescuerOfStolenBikes
https://x.com/Aaron_Wacker/status/1857640877986033980?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1857640877986033980%7Ctwgr%5E203a5022b0eb4c41ee8c1dd9f158330216ac5be1%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Furl%3Dhttps%3A%2F%2Ftwitter.com%2FAaron_Wacker%2Fstatus%2F1857640877986033980
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```
#GPT #Claude #Huggingface
@OpenAI
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https://youtu.be/OMCRRueMhdI | {
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"value": "Weโre excited to release Abstract2Appendix v1 10K , a high-quality dataset crafted to enhance the long-context capabilities of Large Language Models (LLMs). This dataset combines thousands of peer reviews from NeurIPS 2023, EMNLP 2023, TMLR, and ICLR 2023, making it a treasure trove of detailed feedback, critical reasoning, and structured academic insights. Our experiments showed that this dataset increased long context ability of phi-3 models! ",
"raw": "Weโre excited to release Abstract2Appendix v1 10K , a high-quality dataset crafted to enhance the long-context capabilities of Large Language Models (LLMs). This dataset combines thousands of peer reviews from NeurIPS 2023, EMNLP 2023, TMLR, and ICLR 2023, making it a treasure trove of detailed feedback, critical reasoning, and structured academic insights. Our experiments showed that this dataset increased long context ability of phi-3 models! ",
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"value": "\tโข\tExpert Reviews: Aggregated from 3โ6 reviews per paper, capturing the most insightful and constructive content.",
"raw": "\tโข\tExpert Reviews: Aggregated from 3โ6 reviews per paper, capturing the most insightful and constructive content.",
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"value": "\tโข\tRich Metadata: we have aggregated the reviews, and also included full parsed paper",
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"value": "\tโข\tLLM Ready: Perfect for fine-tuning (We did dpo and sft) ",
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"value": "\tโข\tFine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).",
"raw": "\tโข\tFine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).",
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"value": "\tโข\tBenchmarking zero-shot and long-context comprehension capabilities.",
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"value": "This dataset is based on the methodology described in our recent paper, โAbstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilitiesโ. Check it out for more details! ",
"raw": "This dataset is based on the methodology described in our recent paper, โAbstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilitiesโ. Check it out for more details! ",
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] | Weโre excited to release Abstract2Appendix v1 10K , a high-quality dataset crafted to enhance the long-context capabilities of Large Language Models (LLMs). This dataset combines thousands of peer reviews from NeurIPS 2023, EMNLP 2023, TMLR, and ICLR 2023, making it a treasure trove of detailed feedback, critical reasoning, and structured academic insights. Our experiments showed that this dataset increased long context ability of phi-3 models!
๐ Key Highlights:
โข Expert Reviews: Aggregated from 3โ6 reviews per paper, capturing the most insightful and constructive content.
โข Rich Metadata: we have aggregated the reviews, and also included full parsed paper
โข LLM Ready: Perfect for fine-tuning (We did dpo and sft)
๐ฏ Use Cases:
โข Fine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).
โข Benchmarking zero-shot and long-context comprehension capabilities.
๐ Explore the dataset: https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k
This dataset is based on the methodology described in our recent paper, โAbstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilitiesโ. Check it out for more details! https://arxiv.org/abs/2411.05232 | {
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"value": "๐ข For those who are interested in extracting information about โ๏ธ authors from texts, happy to share personal ๐น on Reading Between the lines: adapting ChatGPT-related systems ๐ค for Implicit Information Retrieval National ",
"raw": "๐ข For those who are interested in extracting information about โ๏ธ authors from texts, happy to share personal ๐น on Reading Between the lines: adapting ChatGPT-related systems ๐ค for Implicit Information Retrieval National ",
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"value": "๐ In this talk, we refer to IIR as such information that is indirectly expressed by โ๏ธ author / ๐จ character / patient / any other entity.",
"raw": "๐ In this talk, we refer to IIR as such information that is indirectly expressed by โ๏ธ author / ๐จ character / patient / any other entity.",
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"value": "๐ I cover the 1๏ธโฃ pre-processing and 2๏ธโฃ reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.",
"raw": "๐ I cover the 1๏ธโฃ pre-processing and 2๏ธโฃ reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.",
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"value": "In pictures below, sharing the quick takeaways on the pipeline construction and experiment results ๐งช",
"raw": "In pictures below, sharing the quick takeaways on the pipeline construction and experiment results ๐งช",
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"value": "Related paper cards:",
"raw": "Related paper cards:",
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] | ๐ข For those who are interested in extracting information about โ๏ธ authors from texts, happy to share personal ๐น on Reading Between the lines: adapting ChatGPT-related systems ๐ค for Implicit Information Retrieval National
Youtube: https://youtu.be/nXClX7EDYbE
๐ In this talk, we refer to IIR as such information that is indirectly expressed by โ๏ธ author / ๐จ character / patient / any other entity.
๐ I cover the 1๏ธโฃ pre-processing and 2๏ธโฃ reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.
In pictures below, sharing the quick takeaways on the pipeline construction and experiment results ๐งช
Related paper cards:
๐ emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay
๐ sentiment-analysis: https://nicolay-r.github.io/#ljom2024
Models:
https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
๐ PS: I got a hoppy for advetising HPMoR โจ ๐
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] | Hello Hugging Face Community,
I'd like to share here a bit more about our Deep Learning Containers (DLCs) we built with Google Cloud, to transform the way you build AI with open models on this platform!
With pre-configured, optimized environments for PyTorch Training (GPU) and Inference (CPU/GPU), Text Generation Inference (GPU), and Text Embeddings Inference (CPU/GPU), the Hugging Face DLCs offer:
โก Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration.
๐ ๏ธ Hassle-free environment setup, no more dependency issues.
๐ Seamless updates to the latest stable versions.
๐ผ Streamlined workflow, reducing dev and maintenance overheads.
๐ Robust security features of Google Cloud.
โ๏ธ Fine-tuned for optimal performance, integrated with GKE and Vertex AI.
๐ฆ Community examples for easy experimentation and implementation.
๐ TPU support for PyTorch Training/Inference and Text Generation Inference is coming soon!
Find the documentation at https://huggingface.co/docs/google-cloud/en/index
If you need support, open a conversation on the forum: https://discuss.huggingface.co/c/google-cloud/69 | {
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Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.",
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] | Hello Hugging Face community!
I wanted to introduce myself and my company @Overlaiapp. We are a collective of filmmakers, photographers, and AI engineers working on high resolution (8K+) training data.
We plan to share a lot of our datasets with the community and are kicking things off with two curated datasets:
- https://huggingface.co/datasets/Overlaiai/OregonCoastin4K
- https://huggingface.co/datasets/Overlaiai/SubArcticPolarBear
Overlai.ai Dataset Features
๐ฅ Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.
๐ธ Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.
๐ Detailed Metadata: Every clip is paired with structured metadata, including creative descriptions, precise camera movements, lens information, field of view calculations, and shot settings, ensuring AI models can fully understand and replicate real-world cinematography with accuracy.
โ๏ธ Consistency: Re-thinking training data at the point of capture by "overshooting" a subject, enabling models to learn more nuanced relationships and views across scenes.
๐
Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.
๐ Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training. | {
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] | Animaker / Anime Engine : ๐ค
Only on๐: https://huggingface.co/strangerzonehf
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"value": "๐ค Ever heard of watermarking? It's a technique that allows you to mark in an image its original source. It's our best shield against AI-generated deepfakes, or content stolen from artists! ๐จ ",
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"raw": "๐ญ Watermarking systems are actually a pair of models: a watermark embedder that applies the watermark on the image, and its corresponding decoder that should detect the original watermark.",
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"value": "โ But current methods were very limited: they can only apply and detect the watermark on your image as a whole. So, if you're an attacker it's easy to break: just crop it! add text on top! or whatever, really, anything would work to break the watermark.",
"raw": "โ But current methods were very limited: they can only apply and detect the watermark on your image as a whole. So, if you're an attacker it's easy to break: just crop it! add text on top! or whatever, really, anything would work to break the watermark.",
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"raw": "So to withstand real-world attacks, they decided to make a watermarking model that would also work on any sub-part of the image. It's a real paradigm shift: they consider watermarking not as an image classification task, but as an image segmentation task!",
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"value": "๐๏ธ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ",
"raw": "๐๏ธ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ",
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"value": "โธ The \"Embedder\" (a variational autoencoder + embedder, 1.1M parameters in total) encodes a n-bit message into a watermark signal that is added to the original image",
"raw": "โธ The \"Embedder\" (a variational autoencoder + embedder, 1.1M parameters in total) encodes a n-bit message into a watermark signal that is added to the original image",
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"value": "โธ [Only during training] The \"Augmenter\" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.",
"raw": "โธ [Only during training] The \"Augmenter\" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.",
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"value": "โธ The \"Extractor\" (a vision transformer, or ViT, with 96M parameters) then re-extracts the message from the distorted image, by predicting a (1+n) vector per pixel to predict the watermarked parts and decode corresponding messages.",
"raw": "โธ The \"Extractor\" (a vision transformer, or ViT, with 96M parameters) then re-extracts the message from the distorted image, by predicting a (1+n) vector per pixel to predict the watermarked parts and decode corresponding messages.",
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"value": "The performance blows existing models out of the water, they even created new tasks (segmentation-related) just to grok them!",
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] | ๐ ๐ฒ๐๐ฎ ๐๐ฒ๐ฎ๐บ ๐ท๐๐๐ ๐ฑ๐ฟ๐ผ๐ฝ๐ฝ๐ฒ๐ฑ ๐๐ต๐ฒ ๐ณ๐ถ๐ฟ๐๐ ๐ช๐ฎ๐๐ฒ๐ฟ๐บ๐ฎ๐ฟ๐ธ๐ถ๐ป๐ด ๐บ๐ผ๐ฑ๐ฒ๐น ๐๐ต๐ฎ๐ ๐ป๐ผ๐ ๐ฒ๐ฑ๐ถ๐ ๐ฐ๐ฎ๐ป ๐ฏ๐ฟ๐ฒ๐ฎ๐ธ!๐ก๏ธ
๐ค Ever heard of watermarking? It's a technique that allows you to mark in an image its original source. It's our best shield against AI-generated deepfakes, or content stolen from artists! ๐จ
๐ญ Watermarking systems are actually a pair of models: a watermark embedder that applies the watermark on the image, and its corresponding decoder that should detect the original watermark.
โ But current methods were very limited: they can only apply and detect the watermark on your image as a whole. So, if you're an attacker it's easy to break: just crop it! add text on top! or whatever, really, anything would work to break the watermark.
A team of researchers at Meta was not happy with this. ๐ค
So to withstand real-world attacks, they decided to make a watermarking model that would also work on any sub-part of the image. It's a real paradigm shift: they consider watermarking not as an image classification task, but as an image segmentation task!
๐๏ธ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ
โธ The "Embedder" (a variational autoencoder + embedder, 1.1M parameters in total) encodes a n-bit message into a watermark signal that is added to the original image
โธ [Only during training] The "Augmenter" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.
โธ The "Extractor" (a vision transformer, or ViT, with 96M parameters) then re-extracts the message from the distorted image, by predicting a (1+n) vector per pixel to predict the watermarked parts and decode corresponding messages.
The performance blows existing models out of the water, they even created new tasks (segmentation-related) just to grok them!
Gerat work @pierrefdz and @tomsander1998!
Paper here ๐ https://huggingface.co/papers/2411.07231 | {
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] | Added @amphion MaskGCT & @hexgrad StyleTTS fine tuned model by the name of kokoro to the forked TTS Arena Space. If things keep up from what is seen in the preliminary results, then these two may end up in the TOP 5 of all TTS models. ๐ค๏ธ๐๏ธ
https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena
https://huggingface.co/spaces/Svngoku/maskgct-audio-lab
https://huggingface.co/spaces/hexgrad/Kokoro-TTS
I chose @Svngoku 's forked HF space over amphion's due to the overly high ZeroGPU duration demand on the latter. 300s!
https://huggingface.co/spaces/amphion/maskgct
Had to remove @mrfakename 's MetaVoice-1B Space from the available models as that space has been down for quite some time. ๐ค๏ธ
https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1
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] | ๐ Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases!
We're thrilled to announce the release of SauerkrautLM-v2-14b in two specialized versions: https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT and https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO. Built on the robust Qwen2.5-14B foundation, these models represent a significant leap forward in multilingual AI capabilities.
๐ฌ Technical Breakthroughs:
๐ Innovative three-phase Fine-Tuning approach
๐ Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance
๐ Balance of German and English language capabilities
๐ Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620
๐ฉ๐ช German Language Excellence:
What sets this release apart is our unique achievement in simultaneously improving both German and English capabilities. Through our specialized training approach with over 1.2B tokens across two phases, we've managed to:
๐ Enhance German language understanding and generation (SFT Version > DPO Version)
๐ Maintain authentic German linguistic nuances
๐ Improve cross-lingual capabilities
๐ Preserve cultural context awareness
๐ Training Innovation:
Our three-phase approach targeted specific layer percentages (15%, 20% and 25%) with carefully curated datasets, including:
๐ Mathematics-focused content (proprietary classifier-selected)
๐ High-quality German training data
๐ Specialized function calling datasets
๐ Premium multilingual content
๐ Community Contribution:
We're also releasing two new datasets in a few days:
1๏ธโฃ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples
2๏ธโฃ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling
Thank you to our incredible community and partners who have supported us throughout this journey. Here's to another year of AI innovation!ย ๐ | {
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"value": "๐ Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actuallyโฆ Works ๐คทโโ๏ธ",
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"value": "Let me introduce CaSIL โ the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response thatโs (surprisingly) meaningful to a human.",
"raw": "Let me introduce CaSIL โ the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response thatโs (surprisingly) meaningful to a human.",
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"value": "Iโve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. Itโs built without agent frameworksโjust good ol' Python and mathโand it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Hereโs a quick peek at the steps:",
"raw": "Iโve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. Itโs built without agent frameworksโjust good ol' Python and mathโand it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Hereโs a quick peek at the steps:",
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"value": "Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up.",
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"value": "Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections.",
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"value": "Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result.",
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"value": "Does it work? Yes! And in record time, too. Admittedly, the code is roughโtwo days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; itโs a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.",
"raw": "Does it work? Yes! And in record time, too. Admittedly, the code is roughโtwo days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; itโs a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.",
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] | Early Morning Before Work Project:
๐ Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actuallyโฆ Works ๐คทโโ๏ธ
Let me introduce CaSIL โ the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response thatโs (surprisingly) meaningful to a human.
Iโve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. Itโs built without agent frameworksโjust good ol' Python and mathโand it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Hereโs a quick peek at the steps:
โจ How CaSIL Works:
Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up.
Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections.
Context Integration: Adds historical or contextual knowledge, bringing a bit of depth and relevance.
Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result.
Does it work? Yes! And in record time, too. Admittedly, the code is roughโtwo days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; itโs a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.
๐ Explore the repo here: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers
๐ Example outputs: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md | {
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915742231261639 | [
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"value": "I'll have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) !",
"raw": "I'll have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) !",
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] | I just shipped `retrain-pipelines 0.1.1` today. The doc is also pimped compared to previous release. That was clearly not mature then.
I'll have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) !
In the meantime, you may enjoy retrying this :
https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab | {
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"value": "periodic reminder : if you are experiencing โ ๏ธ500 errors โ ๏ธ or โ ๏ธ abnormal ",
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"raw": "we have a thread ๐๐ป ",
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"value": "if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps ๐ค๐ค๐ค",
"raw": "if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps ๐ค๐ค๐ค",
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] | ๐๐ปโโ๏ธhey there folks,
periodic reminder : if you are experiencing โ ๏ธ500 errors โ ๏ธ or โ ๏ธ abnormal `spaces` behavior on load or launch โ ๏ธ
we have a thread ๐๐ป https://discord.com/channels/879548962464493619/1295847667515129877
if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps ๐ค๐ค๐ค | {
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] | Hello researchers! Here are scripts to generate reviews on HF Daily Papers:
๐ https://github.com/averkij/top_papers
โ๏ธ Works on GitHub Actions
๐ค Claude, GPT-4o, FLUX
๐ Multiple languages
๐ Classification by 38 topics (#agents, #multimodal, #plp, etc.)
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The emulator is capable of predicting
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] | I've released several new Hugging Face Spaces.
My primary objective is to create consistent character facial animation using image-to-image techniques:
https://huggingface.co/spaces/Akjava/CreateConsistentCharacterFacialAnimationWithImg2Img
A short-term goal is create simple talk-head animation.
WebP-3-Frame-Talking-Animation
https://huggingface.co/spaces/Akjava/AIDiagramChatWithVoice-FaceCharacter
[Space]
- GPU tools
Flux1-schnell img2img
https://huggingface.co/spaces/Akjava/flux1-schnell-img2img
Flux1-schnell Inpaint with mask-file
https://huggingface.co/spaces/Akjava/flux1-schnell-img2img
- Tiny CPU tools
WebP-3F-TH - create webp animation from 3 images
OpenCV-Inapint - classic inpaint
Whitebalance - simple white balance
Paste Image - just paste image with mask
WebP Resize Convert - resize and convert webp-animation
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] | Forget any document retrievers, use ColPali ๐ฅ๐ฅ
Document retrieval is done through OCR + layout detection, but you are losing a lot of information in between, stop doing that! ๐ค
ColPali uses a vision language model, which is better in doc understanding ๐
ColPali: https://huggingface.co/vidore/colpali (mit license!)
Blog post: https://huggingface.co/blog/manu/colpali
The authors also released a new benchmark for document retrieval:
ViDoRe Benchmark: https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d
ViDoRe Leaderboard: https://huggingface.co/spaces/vidore/vidore-leaderboard
ColPali marries the idea of modern vision language models with retrieval ๐ค
The authors apply contrastive fine-tuning to SigLIP on documents, and pool the outputs (they call it BiSigLip). Then they feed the patch embedding outputs to PaliGemma and create BiPali ๐๏ธ
BiPali natively supports image patch embeddings to an LLM, which enables leveraging the ColBERT-like late interaction computations between text tokens and image patches (hence the name ColPali!) ๐คฉ
The authors created the ViDoRe benchmark by collecting PDF documents and generate queries from Claude-3 Sonnet.
ColPali seems to be the most performant model on ViDoRe. Not only this, but is way faster than traditional PDF parsers too! | {
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"raw": "๐ Added to the Avatars Collection: ",
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"value": "๐ Keywords: #LivePortrait #PortraitAnimation #ComputerVision #MachineLearning #DeepLearning #ComputerGraphics #FacialAnimation #GenerativeAI #RealTimeRendering #AI",
"raw": "๐ Keywords: #LivePortrait #PortraitAnimation #ComputerVision #MachineLearning #DeepLearning #ComputerGraphics #FacialAnimation #GenerativeAI #RealTimeRendering #AI",
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] | ๐ฅ๐ญ๐ New Research Alert - LivePortrait (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ๐
๐ Description: LivePortrait is an efficient video-driven portrait animation framework that uses implicit keypoints and stitching/retargeting modules to generate high-quality, controllable animations from a single source image.
๐ฅ Authors: @cleardusk, Dingyun Zhang, Xiaoqiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang
๐ค Demo: https://huggingface.co/spaces/KwaiVGI/LivePortrait
๐ Paper: https://huggingface.co/papers/2407.03168
๐ Github Page: https://liveportrait.github.io/
๐ Repository: https://github.com/KwaiVGI/LivePortrait
๐ฅ Model ๐ค: https://huggingface.co/KwaiVGI/LivePortrait
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
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] | Hello.
I'm excited to announce that 'LivePortrait' has been released, and we've developed an advanced application service using it. We've made this service available for free on our Discord server (https://discord.gg/openfreeai). To achieve this, we've deployed four Nvidia H100 GPUs and optimized our code for multi-GPU processing.
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The process is extremely simple, and the results are impressively realistic. Experience this fantastic service for yourself, and stay tuned for more exciting AI services we plan to introduce in the future.
Open Service link: https://discord.gg/openfreeai
Channel Direct Link: https://discord.com/channels/1228254992729767996/1259659402118692896 | {
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] | # LivePortrait 1-Click Installers and full tutorials for Windows and Cloud (useful for Mac users) (Massed Compute, RunPod and a free Kaggle Account)
I know there are a lot of experts around here that can install and use easily. But I have prepared solid tutorials for newbies and shown how to use this amazing top quality app LivePortrait. I have to say congrats to the developers.
I am also a researcher you can see my LinkedIn profile here but recently I am shifted into AI lectures : https://www.linkedin.com/in/furkangozukara/
Both Windows and Cloud tutorial has manually written (100% accurate) captions / subtitles. Also both have manually written by me very detailed video chapters.
Windows LivePortrait Tutorial : https://youtu.be/FPtpNrmuwXk
Cloud LivePortrait Tutorial : Massed Compute, RunPod & Kaggle : https://youtu.be/wG7oPp01COg
## Windows LivePortrait Tutorial Video Chapters
- 0:00 Introduction to LivePortrait: A cutting-edge open-source application for image-to-animation conversion
- 2:20 Step-by-step guide for downloading and installing the LivePortrait Gradio application on your device
- 3:27 System requirements and installation process for LivePortrait
- 4:07 Verifying the successful installation of required components
- 5:02 Confirming installation completion and preserving installation logs
- 5:37 Initiating the LivePortrait application post-installation
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Classes selection support added",
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Classes selection support added",
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Live FPS display in the sidebar ",
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Live FPS display in the sidebar ",
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Webcam and video support added",
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Webcam and video support added",
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Confidence and NMS threshold option to modify.",
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Confidence and NMS threshold option to modify.",
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Segmentation, detection, and pose models support added.",
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Segmentation, detection, and pose models support added.",
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] | ๐จ New Release: ultralytics8.2.51
๐บLive Space : https://huggingface.co/spaces/prithivMLmods/YOLO-VIDEO , Duplicate the Space to avoid queuing issues.
๐บT4 Colab : https://colab.research.google.com/drive/1BKgFUfk2Me1cSPFmbtZSVCn_4cYImPO-?au
๐๐ปFor HPC, use A100/T4 under controlled conditions.
๐๐ปSpeed Estimation, Object Counting, Distance Calculation, Workout Monitoring, Heatmaps,etc.
Ultralytics dropped the YOLOv8 - #Ultralytics 8.2.51 ๐ฅ, YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
๐ https://pypi.org/project/ultralytics/8.2.58/
๐More Features You can try:
โ
Classes selection support added
โ
Live FPS display in the sidebar
โ
Webcam and video support added
โ
Confidence and NMS threshold option to modify.
โ
Segmentation, detection, and pose models support added.
๐Ultralytics Live inference: https://docs.ultralytics.com/guides/streamlit-live-inference/
```
from ultralytics import solutions
solutions.inference()
### Make sure to run the file using command `streamlit run <file-name.py>`
```
โกyolo streamlit-predict
๐๐ปAdvantages of Live Inference
โ๏ธ Seamless Real-Time Object Detection: Streamlit combined with YOLOv8 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.
โ๏ธEfficient Resource Utilization: YOLOv8 optimized algorithm ensure high-speed processing with minimal computational resources.
๐Ultralytics feature Models: https://docs.ultralytics.com/models/, Ultralytics new Solutions: https://docs.ultralytics.com/solutions/
๐๐ปOfficial Documentation:
Ultralytics YOLOv8 Documentation: Refer to the official YOLOv8 documentation for comprehensive guides and insights on various computer vision tasks and projects. ๐ https://docs.ultralytics.com/
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] | Check @lllyasviel Latest research for making videos of still images using hand drawing keyframes:
https://lllyasviel.github.io/pages/paints_undo/
I imported his work to gradio space here
https://huggingface.co/spaces/MohamedRashad/PaintsUndo | {
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] | ๐ข Exciting News! Our latest paper "ChartGemma" is out! ๐
๐งต1/3: ChartGemma overcomes existing chart models key limitations that rely too much on data tables. Instead, it is trained on data generated directly from chart images, capturing crucial visual trends๐ธ๐
๐งต2/3: ChartGemma builds upon PaliGemma from Google Research and is fine-tuned on a high-quality visual instruction tuning dataset generated from Gemini Flash 1.5. ๐๐
๐งต3/3: Achieves state-of-the-art results in chart summarization, question answering, and fact-checking tasks. ๐
๐ It can also generate more accurate and realistic chart summaries. ๐๐
Our model and data are publicly available. We also have a cool web demo. Check it out! ๐
Demo: https://huggingface.co/spaces/ahmed-masry/ChartGemma
Code: https://github.com/vis-nlp/ChartGemma
Paper: https://huggingface.co/papers/2407.04172 | {
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"raw": "Features:",
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"value": "- Personal info anonymized ๐ต๏ธโโ๏ธ",
"raw": "- Personal info anonymized ๐ต๏ธโโ๏ธ",
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] | How's 2024 been so far? ๐ Well, we've got the data to find out!
New dataset just dropped on the Hub: 12.5 million Reddit comments from 2024! ๐๐ฃ๏ธ
Features:
- Personal info anonymized ๐ต๏ธโโ๏ธ
- Language detection ๐
- Token count ๐ข
- NSFW filtering ๐ซ
Most popular post? Classic AITA drama: "AITA for 'ruining Christmas' and being upset the only gifts I got from my family were 'joke gifts'" ๐๐
Some things never change, huh?
Dive in: https://huggingface.co/datasets/OpenCo7/UpVoteWeb | {
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] | Just updated the Journalists on ๐ค community with two new AI tools! ๐๐
Check them out:
- Free video transcription & smart summary tool https://huggingface.co/spaces/artificialguybr/Video-Transcription-Smart-Summary
- ChartGemma - next-level chart analysis focusing on visual trends https://huggingface.co/spaces/ahmed-masry/ChartGemma
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There's a new space out in the wild! https://huggingface.co/spaces/as-cle-bert/self-reviewing-coding-assistant ๐ฆ
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Have fun! ๐ | {
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That's very simple with an agent!",
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That's very simple with an agent!",
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] | One more cookbook:
Agent for self-correcting Text-to-SQL ๐งโ๐ป
What if the query generated by your Text-to-SQL pipeline is correct SQL but returns wrong results?
๐ We need to add a critique step
โ
That's very simple with an agent!
Check out the notebook! ๐
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] | Is a full-featured Mac sufficient for AI and ML development compared to NVIDIA GPU systems?
Hello everyone,
We are about to make the decision to purchase a powerful Mac with maximum features for our university. This Mac will primarily serve as a development computer in the field of artificial intelligence (AI) and machine learning (ML) and will be used by a small group of users in the local network. After development, the systems will be transferred to servers that have to withstand higher loads and visitor numbers.
Planned system:
Apple Mac Studio 2023 with M2 Ultra processor
24-core CPU
76-core GPU
32-core NPU (neural engine) for machine learning
128GB RAM
1TB HDD
Our question to you:
Is a fully equipped Mac with the latest SoCs chips and integrated neural engines sufficient for the development of AI and ML systems?
Or should we rather rely on proven Windows/Linux systems with powerful NVIDIA graphics cards?
We already have several NVIDIA graphics cards available at the university:
NVIDIA Tesla T4
NVIDIA 2080Ti
NVIDIA 3080Ti
We are particularly interested in your experiences and assessments of how the performance of the Mac compares to the GPU systems mentioned.
Are there significant differences, especially in the development and training of models?
What difference would you consider to be significant?
For us, a difference of 100% or more would be considered significant.
In other words, computer A (Mac) takes twice as long to calculate as computer B (NVIDIA system).
Many thanks in advance for your answers and experience!
Best regards,
โจOliver | {
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] | Hi everyone,
I am excited to introduce our latest work, LLaMAX. ๐๐๐
LLaMAX is a powerful language model created specifically for multilingual scenarios. Built upon Meta's LLaMA series models, LLaMAX undergoes extensive training across more than 100 languages.
Remarkably, it enhances its multilingual capabilities without compromising its generalization ability, surpassing existing LLMs.
โจHighlights:
๐ LLaMAX supports the 102 languages covered by Flores-101, and its performance in translating between low-resource languages far surpasses other decoder-only LLMs.
๐ Even for languages not covered in Flores-200, LLaMAX still shows significant improvements in translation performance.
๐ By performing simple SFT on English task data, LLaMAX demonstrates impressive multilingual transfer abilities in downstream tasks.
๐ In our paper, we discuss effective methods for enhancing the multilingual capabilities of LLMs during the continued training phase.
We welcome you to use our model and provide feedback.
More Details:
๐ Code: https://github.com/CONE-MT/LLaMAX/
๐ Model: https://huggingface.co/LLaMAX/ | {
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We're thrilled to introduce our latest project: a hand-curated list of the best-curated lists related to artificial intelligence! ๐
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] | Very cool dataset for journalists and historians just dropped: 2.7 million unique public domain U.S. news wire articles (1878-1977) ๐ฐ๐ฐ๏ธ
This is a goldmine for tracking historical events & newspaper coverage trends! An example? If we still wonder whether gender diversity in the media is important... "Only 4.6% of disambiguated entity mentions refer to women, and the most mentioned woman is Golda Meir."
Bonus:
- Locations in these articles are georeferenced
- Topics are tagged using customized neural topic classification
- Named entities are recognized,
- Individuals are disambiguated to Wikipedia using a novel entity disambiguation model
Anyone thinking of cool AI projects with this data? Maybe tracking the spread of news stories over time & space?
๐๐จ ๐๐๐๐ฉ๐๐ซ
๐ Digg into the dataset: https://huggingface.co/datasets/dell-research-harvard/newswire
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] | Running billion parameter models, sometimes we forget what it all is! ๐ค๐ก
Matrix multiplication ๐งฎโจ
While there are multiple plays on memory management and caching to speed it up! ๐๏ธ๐พโก
The naive way of Matrix multiplication becomes even more fascinating the bigger these models get! ๐คฏ๐
QKV for the win! ๐๐๐
GitHub: https://github.com/wentasah/mmul-anim
Slides: https://cw.fel.cvut.cz/wiki/_media/courses/b4m36esw/esw09_2019.pdf ๐๐ | {
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] | Introducing the first two projects on the HFforLegal community: the 'Laws' dataset and the associated search tool based on @nreimers and @tomaarsen's Sentence Transformers library ๐ค
The objective of these two tools is to centralize in a single format a set of rules from different countries and legal systems in order to facilitate NLP in the field of comparative law, enabling more accurate and comprehensive legal analysis across different jurisdictions ๐
Link to the dataset : https://huggingface.co/datasets/HFforLegal/laws
Link to the space: https://huggingface.co/spaces/HFforLegal/laws-retrieval
We need your contributions to enrich this new knowledge base, and you will find in the 'Laws' dataset all the information you need to format your data and submit them to the appropriate split. | {
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Reformulate the query",
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Reformulate the query",
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Critique the retrived content to re-retrieve if needed",
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Critique the retrived content to re-retrieve if needed",
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] | New cookbook!
I show to to make agentic RAG using Transformers Agents.
Compared to vanilla RAG, agentic RAG can:
โ
Reformulate the query
โ
Critique the retrived content to re-retrieve if needed
โก๏ธ Score increase of 8.5%! ๐ช (Llama-3-70B-judge)
Read it here ๐ https://huggingface.co/learn/cookbook/agent_rag | {
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https://huggingface.co/spaces/KwaiVGI/LivePortrait | {
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] | Kolors with VLM support
I've built a space for using Kolors image generation model with captioner models and prompt enhancers.
- Space with VLM and Prompt Enhancer
https://huggingface.co/spaces/gokaygokay/KolorsPlusPlus
- Original Space for model
https://huggingface.co/spaces/gokaygokay/Kolors
- Captioner VLMs
- https://huggingface.co/gokaygokay/sd3-long-captioner-v2
- https://huggingface.co/microsoft/Florence-2-base
- Prompt Enhancers
- https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long
- https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance
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"value": "Our team just dropped something cool! ๐ We've published a new paper on arxiv diving into the foundation model leaderboards across different platforms. We've analyzed the content, operational workflows, and common issues of these leaderboards. From this, we came up with two new concepts: Leaderboard Operations (LBOps) and leaderboard smells.",
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Our team just dropped something cool! ๐ We've published a new paper on arxiv diving into the foundation model leaderboards across different platforms. We've analyzed the content, operational workflows, and common issues of these leaderboards. From this, we came up with two new concepts: Leaderboard Operations (LBOps) and leaderboard smells.
We also put together an awesome list with nearly 300 of the latest leaderboards, development tools, and publishing organizations. You can check it out here: https://github.com/SAILResearch/awesome-foundation-model-leaderboards
If you find it useful or interesting, give us a follow or drop a comment. We'd love to hear your thoughts and get your support! โจ
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] | Excited to announce the release of the community version of our guardrails: WalledGuard-C!
Feel free to use itโcompared to Metaโs guardrails, it offers superior performance, being 4x faster. Most importantly, it's free for nearly any use!
Link: https://huggingface.co/walledai/walledguard-c
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] | BrainGPT - Fun Weekend Project:)
Getting creative with a sci-fi 3D point cloud model of the brain - you prompt the model with questions about AI research frameworks that were deeply inspired by parts of the brain, you get a response with related papers ๐ | {
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] | Introducing: The P-FAF Swarm Encoder! Now that I know you can do it with Swarm algorithms, the sky is the limit, beebee! The hotness AI tools over the past month have been LLM models with self referential feedback. AKA, getting them to do stuff on their own, like create data, transform data, etc. What if I told you that was wholly inefficient to do? What if I could replicate the same things and run it on a calculator? Meet P-FAF Swarm Encoder, your first real world proof that this is beyond viable. Plenty more proof to come!
Colab Notebook to try it yourself:
https://colab.research.google.com/drive/14CRumDep0SEubEiG3DCOCuGNGeZcsUkO?usp=sharing
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"value": "๐ค A vital question that every developer may have asked themselves in the last three years is: how can we improve AI code generation?",
"raw": "๐ค A vital question that every developer may have asked themselves in the last three years is: how can we improve AI code generation?",
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"value": "๐ In my last Community blog post, I talk about Codium AI's AlphaCodium and how they tried to enhance LLMs coding skills with flow engineering: ",
"raw": "๐ In my last Community blog post, I talk about Codium AI's AlphaCodium and how they tried to enhance LLMs coding skills with flow engineering: ",
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] | ๐ค Hi HF community!
๐ค A vital question that every developer may have asked themselves in the last three years is: how can we improve AI code generation?
๐ In my last Community blog post, I talk about Codium AI's AlphaCodium and how they tried to enhance LLMs coding skills with flow engineering: https://huggingface.co/blog/as-cle-bert/repetita-iuvant-how-to-improve-ai-code-generation
๐ Enjoy! | {
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"value": "> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.",
"raw": "> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.",
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"value": "- How do different combinations of integration strategies affect LLM-based repository-level code completion?",
"raw": "- How do different combinations of integration strategies affect LLM-based repository-level code completion?",
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"value": "> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.",
"raw": "> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.",
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"value": "- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?",
"raw": "- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?",
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"value": "> Static analysis and RAG are complementary and boost the overall accuracy.",
"raw": "> Static analysis and RAG are complementary and boost the overall accuracy.",
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"value": "- What are the online costs of different integration strategies in LLM-based repository-level code completion?",
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"value": "> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.",
"raw": "> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.",
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"raw": " App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - ",
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] | A new paper titled "STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis" shows the benefits of integrating static analysis with LLMs. (https://arxiv.org/abs/2406.10018)
Authors evaluate 4 key questions:
- How does each static analysis integration strategy perform in LLM-based repository-level code completion?
> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.
- How do different combinations of integration strategies affect LLM-based repository-level code completion?
> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.
- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?
> Static analysis and RAG are complementary and boost the overall accuracy.
- What are the online costs of different integration strategies in LLM-based repository-level code completion?
> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.
In my @owasp App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - https://www.youtube.com/watch?v=Cw4-ZnUNVLs) | {
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Good folks at PyTorch are just wizards ๐งโโ๏ธ. Along with producing one of the best Deep Learning libraries of all time, they just dropped the "PyTorch Documentary" ๐ฅ
A must-watch! It covers the beginning to now:
- Caffe 2 โ
- Torch (with Lua) ๐ฅ
- Tensorflow ๐
- PyTorch ๐ฅ
- Taffe IR (later became ONNX - Open Neural Network Exchange) ๐ง
Full Official PyTorch Documentary: Powering the AI Revolution: https://youtu.be/rgP_LBtaUEc
Interesting quote: "PyTorch does not fight for the fastest performance but the easiest user experience!" ๐
That's what Python ๐ feels like...
You got to thank @Meta for open-sourcing it! ๐ค | {
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"value": "๐ It's one of the trending datasets on Hugging Face. Digging into it is quite fun! I found one that reminds me of several people I know: \"A journalist who covers technology and innovation in the print and digital media industries.\" It helped generate the prompt attached to this post (about which I'd be curious to know your answers ๐).",
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] | ๐ง How to create more diverse, realistic synthetic AI training data?
@TencentAIGC-Lab AI Lab created @proj-persona, a vast collection of 1 billion diverse personas, to help create synthetic data with LLMs that encapsulate a wide array of perspectives, knowledge, experiences, interests, and professions.
These personas were created with automatically curated data, representing approximately 13% of the worldโs total population.
๐ก The authors argue that integrating a persona into data synthesis prompts effectively steers LLMs to adopt specific perspectives, creating unique and relevant synthetic data with minimal effort.
They showcased various practical applications of Persona Hub to demonstrate its effectiveness and versatility in various synthetic data creation scenarios: mathematical and logical reasoning problems, simulating diverse user requests and prompts for LLMs, generating informative and detailed text content across various topics, and more.
๐ It's one of the trending datasets on Hugging Face. Digging into it is quite fun! I found one that reminds me of several people I know: "A journalist who covers technology and innovation in the print and digital media industries." It helped generate the prompt attached to this post (about which I'd be curious to know your answers ๐).
Synthetic data is a hot topic in AI. It will be interesting to see if this research could help make LLMs more robust, versatile, and capable of handling a wide array of real-world scenarios.
๐Explore the dataset: https://huggingface.co/datasets/proj-persona/PersonaHub
๐ Read the paper: https://arxiv.org/pdf/2406.20094 | {
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] | I updated https://huggingface.co/spaces/nroggendorff/llava to be even worse. The point of this space is to point out how bad the base model for Llava is, and how without an image it struggles quite a bit. In the new update, from my testing, even if you ask the model about the image, it won't be able to tell you. I experimented with quite a few things, including an image where all the values are 0 (shoutout np.zeros), an image with the most generic portrait photo I could think of (black hair, brown eyes, plain white shirt, etc..) (generated with https://huggingface.co/spaces/nroggendorff/epicrealismxl), an image that has text that reads "The image you are looking for is unavailable", and much.. much more.. *sigh*. The image I'm ending up with seems to work best. I don't think it would work with other multi-modal LLMs, but I'm not sure. If you find a problem, feel free to message me on the [huggingface discord](http://hf.co/join/discord), or open a pull request. | {
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] | ๐ข Delighted to share the most recent and valuable contributions to the book-related NLP domain ๐ To push forward deeper understanding of the characters ๐จโ๐ฉโ๐งโ๐ฆ from literature novel books itself ๐ by machine learning models ๐ค, releasing the most-accessible version v1.0 of the related workflow, adopted for ParlAI ๐ฆ agents
๐ https://github.com/nicolay-r/book-persona-retriever/tree/v1.0
Feel free to follow / share / comment in order to advance the related direction!
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] | AIโs Cognitive Mirror: The Illusion of Consciousness in the Digital Age
https://empereur-pirate.medium.com/ais-cognitive-mirror-the-illusion-of-consciousness-in-the-digital-age-46f3ddae60a6
This text explores the philosophical implications of AI's potential for spiritual consciousness. It argues that AI, while capable of complex thought processes, lacks true self-awareness due to the absence of sensory perception. The article discusses how human consciousness develops through sensory experiences, contrasting this with AI's purely computational nature. It examines AI as a mirror of human thought, capable of impressive language processing and knowledge synthesis, yet different from human psyche. The text also touches on the spiritual and ethical dimensions of AI development, drawing parallels with historical technological revolutions and religious symbolism. It concludes by considering AI's potential role in decision-making processes and its impact on various fields, while emphasizing the need for ethical safeguards and critical evaluation of AI-generated content. | {
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] | Just released the GitVerse Code Dataset - https://huggingface.co/datasets/nyuuzyou/gitverse-code.
๐ Dataset highlights:
- 30 GB of unique code extracted from over 400 GB of analyzed data
- 9,014 repositories
- 2,804,216 unique code files
- 419 different file types
- Multilingual: various programming languages
๐ Sourced from GitVerse, a Russian GitHub alternative opened in 2024.
Let me know your thoughts. | {
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] | Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis
Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by the Kuaishou Kolors team.
Hugging Face Spaces
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Model Page
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] | Remember the recently released GLM-4 from Tsinghua University ๐
Now we have an open-source version of it continuously trained for multilingual code generation! ๐
It beats CodeLlama 70B (almost 7x size) and is competitive with DeepSeek Coder 33B and Qwen 2 ๐ช
Just like almost every other coding model, it has a 128K context ๐
Supports:
- Code completion ๐๏ธ
- Code generation ๐ ๏ธ
- Code interpreter ๐ก
- Web search ๐
- Function call ๐
- Repository-level code Q&A ๐๏ธ
Benchmarks 48.9 and 40.4 for the complete and instruct tasks of BigCodeBench ๐
It still falls behind DeepSeek-Coder-V2. While this might have fewer parameters, DSC V2 is a MoE model with only ~2B active parameters ๐ค
Good to see more efficient coding LLMs, but DeepSeek-Coder-V2 is just too good ๐
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"value": "The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback. ",
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] | I believe in order to make models reach Human-Level Learning, serious students can start by developing an intelligent neuromorphic agent. We develop an intelligent agent and make it learn about grammar patterns as well as about different word categories through symbolic representations, following which we dwell into making the agent learn about other rules of the Language.
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I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at.
Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at https://octave-x.com/ | {
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"value": "ColPali: A new approach to efficient and intelligent document retrieval ๐",
"raw": "ColPali: A new approach to efficient and intelligent document retrieval ๐",
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"value": "Our latest research paper, \"ColPali: Efficient Document Retrieval with Vision Language Models,\" introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient.",
"raw": "Our latest research paper, \"ColPali: Efficient Document Retrieval with Vision Language Models,\" introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient.",
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"raw": "Key Insights:",
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"value": "๐ก ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities",
"raw": "๐ก ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities",
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"value": "โ๏ธ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings",
"raw": "โ๏ธ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings",
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"value": "๐ The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios",
"raw": "๐ The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios",
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"value": "๐ ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model)",
"raw": "๐ ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model)",
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"value": "โก ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use",
"raw": "โก ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use",
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"value": "๐ ColPali is highly interpretable thanks to patch-based similarity maps",
"raw": "๐ ColPali is highly interpretable thanks to patch-based similarity maps",
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"value": "Dive deeper into ColPali and explore our resources:",
"raw": "Dive deeper into ColPali and explore our resources:",
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] | ColPali: A new approach to efficient and intelligent document retrieval ๐
Our latest research paper, "ColPali: Efficient Document Retrieval with Vision Language Models," introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient.
Key Insights:
๐ก ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities
โ๏ธ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings
๐ The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios
๐ ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model)
โก ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use
๐ ColPali is highly interpretable thanks to patch-based similarity maps
Dive deeper into ColPali and explore our resources:
๐ Full paper: arxiv.org/abs/2407.01449
๐ ๏ธ Datasets, model weights, evaluation code, leaderboard, demos: huggingface.co/vidore
Shoutout to my amazing co-authors Manuel Faysse (@manu) and Hugues Sibille (@HugSib). We are grateful for the invaluable feedback from Bilel Omrani, Gautier Viaud, Celine Hudelot, and Pierre Colombo. This work is sponsored by ILLUIN Technology. โจ | {
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] | ๐๐บ๐ New Research Alert (Avatars Collection)! ๐๐๐
๐ Title: Expressive Gaussian Human Avatars from Monocular RGB Video ๐
๐ Description: The new EVA model enhances the expressiveness of digital avatars by using 3D Gaussians and SMPL-X to capture fine-grained hand and face details from monocular RGB video.
๐ฅ Authors: Hezhen Hu, Zhiwen Fan, Tianhao Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, and Zhangyang Wang
๐ Paper: https://huggingface.co/papers/2407.03204
๐ Github Page: https://evahuman.github.io/
๐ Repository: https://github.com/evahuman/EVA
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
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] | Weclome to use MInference, which leverages the dynamic sparse nature of LLMs' attention, which exhibits some static patterns, to speed up the pre-filling for million tokens LLMs. It first determines offline which sparse pattern each head belongs to, then approximates the sparse index online and dynamically computes attention with the optimal custom kernels. This approach achieves up to a 10x speedup for pre-filling on an A100 while maintaining accuracy with 1M tokens.
For more detail please check,
project page: https://aka.ms/MInference
code: https://github.com/microsoft/MInference
paper: https://huggingface.co/papers/2407.02490
hf demo: https://huggingface.co/spaces/microsoft/MInference | {
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"raw": "Find exactly what you need with filters for:",
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] | New dataset filtering feature just dropped! ๐ค๐
Find exactly what you need with filters for:
- Modalities (text, image, audio, etc.)
- Dataset size
- File format
Try it now: https://huggingface.co/datasets
What other filters would you find useful? Drop your ideas! | {
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] | Hi @jonoirwin! Big fan of https://fastvoiceagent.cerebrium.ai/ ๐ฅ
I'd be super happy to give you a GPU grant to host it on a Space, it would allow more people to discover and use it! | {
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"raw": "6๏ธโฃ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.",
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"raw": "๐ช Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.",
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] | @Omartificial-Intelligence-Space has trained and released 6 Arabic embedding models for semantic similarity. 4 of them outperform all previous models on the STS17 Arabic-Arabic task!
๐ Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset: https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet
6๏ธโฃ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.
๐ช Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.
๐ Outperforms all commonly used multilingual models like https://huggingface.co/intfloat/multilingual-e5-large, https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2, and https://huggingface.co/sentence-transformers/LaBSE.
Check them out here:
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-labse-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka
- https://huggingface.co/Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet
Or the collection with all: https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e
My personal favourite is likely https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka: a very efficient 135M parameters & scores #1 on https://huggingface.co/spaces/mteb/leaderboard.
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I put together my own page of models using their code and LoRA. Enjoy!
https://huggingface.co/spaces/alvdansen/flash-lora-araminta-k-styles
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] | nanoLLaVA-1.5 is here! Same size (1B), better performance ๐ฅ๐ฅ๐ฅ
And it is much more powerful than v1.0
Try it out now on HF Spaces: https://huggingface.co/spaces/qnguyen3/nanoLLaVA
Model: https://huggingface.co/qnguyen3/nanoLLaVA-1.5
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] | Below we experiment with negative merger weighting (-1.0!) using task arithmetic. Merge formula on the model card and in the repo itself.
This model is steered to behave opposite to what MopeyMule demonstrated.
Based on the implications of the merge technique, we also propose Orthogonalized Vector Adaptation (OVA). We also extract a LoRA of the counter-refusal abliteration steering vector.
The resulting merger is not a perfect model, but it's a behaviorally interesting model. The model name was inspired by a Philip K. Dick story.
https://huggingface.co/grimjim/Llama-3-Perky-Pat-Instruct-8B
Refusal vector weights ready for use:
https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-OVA-8B
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] | An Open-source and super-fast alternative to @OpenAI GPT4o is here! ๐
In November last year, Iliad announced a fully open-source-oriented AI lab called @kyutai_labs ๐งช
In this very short time they have released Moshi! An open speech-to-speech model ๐ฃ๏ธ... released publicly even before closed GPT4o (yes, you can try it right now!) ๐
Demo: https://www.moshi.chat/?queue_id=talktomoshi
This is what you expect an intelligent companion to do! ๐ค It is continuously generating responses and listening at the same time with sub 300 ms latency! โฑ๏ธ
This level of engagement is so new, it almost feels like I am under pressure to keep up with it! ๐
While it hallucinates like crazy! ๐คฏ I think fundamentally this is what a true assistant would look like. And did I say they are going to open-source it? ๐
Weights and full technical report are promised to be coming soon! ๐
This is the work of an incredible but just 8 team members!! ๐ฅ๐ | {
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] | As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory.
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] | New #NVIDIA paper: Improving Hyperparameter Optimization with Checkpointed Model Weights
Hyperparameter optimization often dominates the cost of model design. So, we want cheap surrogate functions that approximate model performance to guide our search. Existing methods can train on optimization metadata โ like a trajectory of losses โ to build these surrogates.
In our work, we add the ability to train our hyperparameter optimization surrogates on checkpointed model weights with a graph metanetwork. This allows us to leverage a large, pre-existing source of information that can featurize the architecture, dataset, losses, and optimization procedure.
๐Project page: https://research.nvidia.com/labs/toronto-ai/FMS/
๐จโ๐ป Code for reproduction: https://github.com/NVlabs/forecasting-model-search
๐ Full Paper: https://arxiv.org/abs/2406.18630
Our project was a collaboration between NVIDIAโs Toronto AI Lab and the TAO team.
Check out more work from Toronto AI Lab here: https://research.nvidia.com/labs/toronto-ai/
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"value": "If you remember the Bigscience BLOOM-176B training, Tunji Ruwase and I co-invented this technology for Megatron-Deepspeed in order to enable to quickly scale up and down node topology while continuing training.",
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] | The Universal Checkpointing paper is out! https://arxiv.org/abs/2406.18820
If you remember the Bigscience BLOOM-176B training, Tunji Ruwase and I co-invented this technology for Megatron-Deepspeed in order to enable to quickly scale up and down node topology while continuing training.
Since then the DeepSpeed team continued improving on that and it has now been fully integrated into Deepspeed.
The blog post is here: https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ucp/README.md | {
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"value": "Not a drill and not lying, 100% success rate with GPT 3.5 and Swarm algorithms for AI Agents. GPT 3.5 will create and execute the Swarm algorithms, and they will complete the API call 100% of the time. Here is the Colab, play with it yourself. I have a Github Repo for it all too: ",
"raw": "Not a drill and not lying, 100% success rate with GPT 3.5 and Swarm algorithms for AI Agents. GPT 3.5 will create and execute the Swarm algorithms, and they will complete the API call 100% of the time. Here is the Colab, play with it yourself. I have a Github Repo for it all too: ",
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AI Agents Solved!
Not a drill and not lying, 100% success rate with GPT 3.5 and Swarm algorithms for AI Agents. GPT 3.5 will create and execute the Swarm algorithms, and they will complete the API call 100% of the time. Here is the Colab, play with it yourself. I have a Github Repo for it all too: https://colab.research.google.com/drive/1EF_JmPidwoCd8tEgOChUt6kCX7TBLe20?usp=sharing | {
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"value": " dropped Gemma 2 and now Gemma 2 27B is the highest-ranked open-source LLM on LMSYS Chatbot Arena Leaderboard, beating Meta Llama 3 70B and Alibaba Qwen 2 72B! ๐๐ช",
"raw": " dropped Gemma 2 and now Gemma 2 27B is the highest-ranked open-source LLM on LMSYS Chatbot Arena Leaderboard, beating Meta Llama 3 70B and Alibaba Qwen 2 72B! ๐๐ช",
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"value": "1๏ธโฃ First, it's a challenging model to run. The only reason I could find is soft-capping of logits within the attention for longer context optimizations. And no one was doing that before! ๐คฏ",
"raw": "1๏ธโฃ First, it's a challenging model to run. The only reason I could find is soft-capping of logits within the attention for longer context optimizations. And no one was doing that before! ๐คฏ",
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"value": "2๏ธโฃ Next, the technical report mentions a 2B model... where is it? ๐ค",
"raw": "2๏ธโฃ Next, the technical report mentions a 2B model... where is it? ๐ค",
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"value": "3๏ธโฃ Simple things like a context length of 8192 tokens, the Rotary Position Embeddings (RoPE), and the approximated GeGLU non-linearity are similar to earlier Gemma's. ๐๐",
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"value": "4๏ธโฃ But a lot of new stuff is here like Local Sliding Window and Global Attention: they alternate between them at every layer... don't know why! ๐คทโโ๏ธ and of course Logit soft-capping. ๐ก",
"raw": "4๏ธโฃ But a lot of new stuff is here like Local Sliding Window and Global Attention: they alternate between them at every layer... don't know why! ๐คทโโ๏ธ and of course Logit soft-capping. ๐ก",
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"value": "5๏ธโฃ On-Policy Distillation of Language Models - Knowledge Distillation: Leverage a larger teacher model to train a smaller model (the 9B model). ๐โก๏ธ๐",
"raw": "5๏ธโฃ On-Policy Distillation of Language Models - Knowledge Distillation: Leverage a larger teacher model to train a smaller model (the 9B model). ๐โก๏ธ๐",
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] | ๐ It's been about a week since @Google dropped Gemma 2 and now Gemma 2 27B is the highest-ranked open-source LLM on LMSYS Chatbot Arena Leaderboard, beating Meta Llama 3 70B and Alibaba Qwen 2 72B! ๐๐ช
๐ Here is what a week of Gemma looked like:
1๏ธโฃ First, it's a challenging model to run. The only reason I could find is soft-capping of logits within the attention for longer context optimizations. And no one was doing that before! ๐คฏ
2๏ธโฃ Next, the technical report mentions a 2B model... where is it? ๐ค
3๏ธโฃ Simple things like a context length of 8192 tokens, the Rotary Position Embeddings (RoPE), and the approximated GeGLU non-linearity are similar to earlier Gemma's. ๐๐
4๏ธโฃ But a lot of new stuff is here like Local Sliding Window and Global Attention: they alternate between them at every layer... don't know why! ๐คทโโ๏ธ and of course Logit soft-capping. ๐ก
5๏ธโฃ On-Policy Distillation of Language Models - Knowledge Distillation: Leverage a larger teacher model to train a smaller model (the 9B model). ๐โก๏ธ๐
6๏ธโฃ Model Merging: Combined average models from experiments run with different hyperparameters. ๐งชโ๏ธ
7๏ธโฃ And borrowed Grouped-Query Attention (GQA) from @Meta Llama-3. ๐๐ค
๐ One of the best articles on Gemma 2: https://huggingface.co/blog/gemma2
๐ Technical report: https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf
๐ Models: https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315
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"value": "I've been in the lab, I think one or two of you saw my furtive attempts to create a dolphinized 2b Gemma, which is still waiting for more funding. I get paid in a week. ",
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"value": "It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently!",
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] | Hello!
I've been in the lab, I think one or two of you saw my furtive attempts to create a dolphinized 2b Gemma, which is still waiting for more funding. I get paid in a week. `</3`
Once that funding ran out, I dropped my last pinch of API credits to work on this:
https://huggingface.co/datasets/DigitalClockwork/spatial_instruct_v1
It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently!
Have a great day, and enjoy! This was one fun! ๐ค
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530578558895023 | [
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"type": "text",
"value": "๐ฌ๐ผ๐ ๐ฑ๐ผ๐ป'๐ ๐ป๐ฒ๐ฒ๐ฑ \"๐ณ๐๐ป๐ฐ๐๐ถ๐ผ๐ป ๐ฐ๐ฎ๐น๐น๐ถ๐ป๐ด ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด\" ๐๐ผ ๐ฏ๐๐ถ๐น๐ฑ ๐ด๐ผ๐ผ๐ฑ ๐ฎ๐ด๐ฒ๐ป๐๐ โ",
"raw": "๐ฌ๐ผ๐ ๐ฑ๐ผ๐ป'๐ ๐ป๐ฒ๐ฒ๐ฑ \"๐ณ๐๐ป๐ฐ๐๐ถ๐ผ๐ป ๐ฐ๐ฎ๐น๐น๐ถ๐ป๐ด ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด\" ๐๐ผ ๐ฏ๐๐ถ๐น๐ฑ ๐ด๐ผ๐ผ๐ฑ ๐ฎ๐ด๐ฒ๐ป๐๐ โ",
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"value": "It's trendy to share models \"fine-tuned for function calling\"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.",
"raw": "It's trendy to share models \"fine-tuned for function calling\"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.",
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"value": "To name only a few:",
"raw": "To name only a few:",
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"value": "๐ฆโโฌ Nexusflow/๐ก๐ฒ๐
๐๐๐ฅ๐ฎ๐๐ฒ๐ป-๐ฉ๐ฎ-๐ญ๐ฏ๐",
"raw": "๐ฆโโฌ Nexusflow/๐ก๐ฒ๐
๐๐๐ฅ๐ฎ๐๐ฒ๐ป-๐ฉ๐ฎ-๐ญ๐ฏ๐",
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"value": "โ CohereForAI/๐ฐ๐ฐ๐ฎ๐ถ-๐ฐ๐ผ๐บ๐บ๐ฎ๐ป๐ฑ-๐ฟ-๐ฝ๐น๐๐",
"raw": "โ CohereForAI/๐ฐ๐ฐ๐ฎ๐ถ-๐ฐ๐ผ๐บ๐บ๐ฎ๐ป๐ฑ-๐ฟ-๐ฝ๐น๐๐",
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"value": "โต๏ธ mistralai/๐ ๐ถ๐
๐๐ฟ๐ฎ๐น-๐ด๐
๐ฎ๐ฎ๐-๐๐ป๐๐๐ฟ๐๐ฐ๐-๐๐ฌ.๐ญ",
"raw": "โต๏ธ mistralai/๐ ๐ถ๐
๐๐ฟ๐ฎ๐น-๐ด๐
๐ฎ๐ฎ๐-๐๐ป๐๐๐ฟ๐๐ฐ๐-๐๐ฌ.๐ญ",
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"value": "\"Fine-tuned for function-calling\" generally means \"fine-tuned to generate function calls in correct JSON for extremely simple tasks\". In other terms, it means \"improve the formatting of the tool calls\".",
"raw": "\"Fine-tuned for function-calling\" generally means \"fine-tuned to generate function calls in correct JSON for extremely simple tasks\". In other terms, it means \"improve the formatting of the tool calls\".",
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"value": "Yet I discovered two things while improving Transformers Agents:",
"raw": "Yet I discovered two things while improving Transformers Agents:",
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"value": "๐ง Even when used as JSON agents, these fine-tuned models don't perform very well",
"raw": "๐ง Even when used as JSON agents, these fine-tuned models don't perform very well",
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๐๐ค๐ค๐ ๐๐๐จ๐ ๐ข๐ค๐๐๐ก๐จ ๐ฅ๐๐ง๐๐ค๐ง๐ข ๐๐๐ฉ๐ฉ๐๐ง ๐ฌ๐๐ฉ๐๐ค๐ช๐ฉ ๐๐ฃ๐ฎ ๐๐๐ฃ๐-๐ฉ๐ช๐ฃ๐๐ฃ๐, ๐๐ช๐จ๐ฉ ๐ฅ๐ก๐๐๐ฃ ๐ฅ๐ง๐ค๐ข๐ฅ๐ฉ๐๐ฃ๐. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet) ",
"raw": "๐
๐๐ค๐ค๐ ๐๐๐จ๐ ๐ข๐ค๐๐๐ก๐จ ๐ฅ๐๐ง๐๐ค๐ง๐ข ๐๐๐ฉ๐ฉ๐๐ง ๐ฌ๐๐ฉ๐๐ค๐ช๐ฉ ๐๐ฃ๐ฎ ๐๐๐ฃ๐-๐ฉ๐ช๐ฃ๐๐ฃ๐, ๐๐ช๐จ๐ฉ ๐ฅ๐ก๐๐๐ฃ ๐ฅ๐ง๐ค๐ข๐ฅ๐ฉ๐๐ฃ๐. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet) ",
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"value": "๐ The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: ๐ฐ๐๐๐๐๐ฟ๐๐๐๐๐๐๐ด๐๐๐๐ and ๐ฐ๐๐๐๐๐ด๐ก๐๐๐๐๐๐๐๐ด๐๐๐๐ are the ones caused by incorrect formatting.",
"raw": "๐ The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: ๐ฐ๐๐๐๐๐ฟ๐๐๐๐๐๐๐ด๐๐๐๐ and ๐ฐ๐๐๐๐๐ด๐ก๐๐๐๐๐๐๐๐ด๐๐๐๐ are the ones caused by incorrect formatting.",
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"value": "โค As you can see, their count is already close to 0!",
"raw": "โค As you can see, their count is already close to 0!",
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"value": "And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that \"function calling fine-tuning\" is not necessary!",
"raw": "And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that \"function calling fine-tuning\" is not necessary!",
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"value": "The hardest thing to get right in an agent is still to ๐ฅ๐ก๐๐ฃ ๐๐ค๐ค๐ ๐ฉ๐๐จ๐ -๐จ๐ค๐ก๐ซ๐๐ฃ๐ ๐ฉ๐ง๐๐๐๐๐ฉ๐ค๐ง๐๐๐จ ๐ค๐ซ๐๐ง ๐จ๐๐ซ๐๐ง๐๐ก ๐จ๐ฉ๐๐ฅ๐จ.",
"raw": "The hardest thing to get right in an agent is still to ๐ฅ๐ก๐๐ฃ ๐๐ค๐ค๐ ๐ฉ๐๐จ๐ -๐จ๐ค๐ก๐ซ๐๐ฃ๐ ๐ฉ๐ง๐๐๐๐๐ฉ๐ค๐ง๐๐๐จ ๐ค๐ซ๐๐ง ๐จ๐๐ซ๐๐ง๐๐ก ๐จ๐ฉ๐๐ฅ๐จ.",
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] | ๐ฌ๐ผ๐ ๐ฑ๐ผ๐ป'๐ ๐ป๐ฒ๐ฒ๐ฑ "๐ณ๐๐ป๐ฐ๐๐ถ๐ผ๐ป ๐ฐ๐ฎ๐น๐น๐ถ๐ป๐ด ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด" ๐๐ผ ๐ฏ๐๐ถ๐น๐ฑ ๐ด๐ผ๐ผ๐ฑ ๐ฎ๐ด๐ฒ๐ป๐๐ โ
It's trendy to share models "fine-tuned for function calling"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.
To name only a few:
๐ฆโโฌ Nexusflow/๐ก๐ฒ๐
๐๐๐ฅ๐ฎ๐๐ฒ๐ป-๐ฉ๐ฎ-๐ญ๐ฏ๐
โ CohereForAI/๐ฐ๐ฐ๐ฎ๐ถ-๐ฐ๐ผ๐บ๐บ๐ฎ๐ป๐ฑ-๐ฟ-๐ฝ๐น๐๐
โต๏ธ mistralai/๐ ๐ถ๐
๐๐ฟ๐ฎ๐น-๐ด๐
๐ฎ๐ฎ๐-๐๐ป๐๐๐ฟ๐๐ฐ๐-๐๐ฌ.๐ญ
"Fine-tuned for function-calling" generally means "fine-tuned to generate function calls in correct JSON for extremely simple tasks". In other terms, it means "improve the formatting of the tool calls".
Yet I discovered two things while improving Transformers Agents:
๐ง Even when used as JSON agents, these fine-tuned models don't perform very well
๐
๐๐ค๐ค๐ ๐๐๐จ๐ ๐ข๐ค๐๐๐ก๐จ ๐ฅ๐๐ง๐๐ค๐ง๐ข ๐๐๐ฉ๐ฉ๐๐ง ๐ฌ๐๐ฉ๐๐ค๐ช๐ฉ ๐๐ฃ๐ฎ ๐๐๐ฃ๐-๐ฉ๐ช๐ฃ๐๐ฃ๐, ๐๐ช๐จ๐ฉ ๐ฅ๐ก๐๐๐ฃ ๐ฅ๐ง๐ค๐ข๐ฅ๐ฉ๐๐ฃ๐. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet)
๐ The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: ๐ฐ๐๐๐๐๐ฟ๐๐๐๐๐๐๐ด๐๐๐๐ and ๐ฐ๐๐๐๐๐ด๐ก๐๐๐๐๐๐๐๐ด๐๐๐๐ are the ones caused by incorrect formatting.
โค As you can see, their count is already close to 0!
And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that "function calling fine-tuning" is not necessary!
The hardest thing to get right in an agent is still to ๐ฅ๐ก๐๐ฃ ๐๐ค๐ค๐ ๐ฉ๐๐จ๐ -๐จ๐ค๐ก๐ซ๐๐ฃ๐ ๐ฉ๐ง๐๐๐๐๐ฉ๐ค๐ง๐๐๐จ ๐ค๐ซ๐๐ง ๐จ๐๐ซ๐๐ง๐๐ก ๐จ๐ฉ๐๐ฅ๐จ.
To improve this, we could:
- Use more powerful base models
- Make tool calling datasets with complex solving trajectories
- Use RL! cc @lvwerra | {
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๐ paper: https://huggingface.co/papers/2303.15764 | {
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"value": "๐ฅ More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution.",
"raw": "๐ฅ More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution.",
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"value": "๐ For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total). ",
"raw": "๐ For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total). ",
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"value": "๐ For PWMs, the baseline is left far behind, but some TFs remain tough nuts to be cracked (see the attached figure 1).",
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"value": "๐ For AAAs, there is a solid improvement over the best-submitted PWMs in A2G, but the G2A discipline remains unpopular (see the attached figure 2). Free hint: this is your chance! ",
"raw": "๐ For AAAs, there is a solid improvement over the best-submitted PWMs in A2G, but the G2A discipline remains unpopular (see the attached figure 2). Free hint: this is your chance! ",
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"value": "๐ก Another free hint: If your model tends to overfit given a limited set of data for some TFs don't forget to use reverse-complement and shift augmentations. Also, don't hesitate to use multitarget models i.e. predicting the binding of multiple TFs at the same time. ",
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"value": "๐ก Last but not least, try to combine knowledge from all accessible experiment types, especially for G2A discipline (ChIP-Seq & genomic HT-SELEX) in a single model! ",
"raw": "๐ก Last but not least, try to combine knowledge from all accessible experiment types, especially for G2A discipline (ChIP-Seq & genomic HT-SELEX) in a single model! ",
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"value": "๐ฃ Finally and importantly, following the requests from the community, we decided to EXTEND the Leaderboard until the final submission deadline. ",
"raw": "๐ฃ Finally and importantly, following the requests from the community, we decided to EXTEND the Leaderboard until the final submission deadline. ",
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"value": "๐๏ธ The final submission deadline is also EXTENDED until Aug 15. The final submission form and details will be posted on the IBIS website in the first half of July, follow our Telegram group and mailing list (see the links at ",
"raw": "๐๏ธ The final submission deadline is also EXTENDED until Aug 15. The final submission form and details will be posted on the IBIS website in the first half of July, follow our Telegram group and mailing list (see the links at ",
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] | ๐ฆ Do you remember IBIS? Not a fancy bird but the open challenge in Inferring Binding Specificities of unexplored human transcription factors. Check our site (https://ibis.autosome.org/) and have a sip of fresh news below.
๐ฅ More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution.
๐ Remember, the training data for Leaderboard and Final are available online, and you are free to mix-and-match it in any combination.
๐ For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total).
๐ For PWMs, the baseline is left far behind, but some TFs remain tough nuts to be cracked (see the attached figure 1).
๐ For AAAs, there is a solid improvement over the best-submitted PWMs in A2G, but the G2A discipline remains unpopular (see the attached figure 2). Free hint: this is your chance!
๐ก Another free hint: If your model tends to overfit given a limited set of data for some TFs don't forget to use reverse-complement and shift augmentations. Also, don't hesitate to use multitarget models i.e. predicting the binding of multiple TFs at the same time.
๐ก Last but not least, try to combine knowledge from all accessible experiment types, especially for G2A discipline (ChIP-Seq & genomic HT-SELEX) in a single model!
๐ฃ Finally and importantly, following the requests from the community, we decided to EXTEND the Leaderboard until the final submission deadline.
๐๏ธ The final submission deadline is also EXTENDED until Aug 15. The final submission form and details will be posted on the IBIS website in the first half of July, follow our Telegram group and mailing list (see the links at https://ibis.autosome.org). | {
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๐ Title: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture ๐
๐ Description: Topo4D is a novel method for automated, high-fidelity 4D head tracking that optimizes dynamic topological meshes and 8K texture maps from multi-view time-series images.
๐ฅ Authors: @Dazz1e, Y. Cheng, @Ryan-sjtu, H. Jia, D. Xu, W. Zhu, Y. Yan
๐
Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: https://huggingface.co/papers/2406.00440
๐ Github Page: https://xuanchenli.github.io/Topo4D/
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] | I've created a Stable Diffusion 3 (SD3) image generation space for convenience. Now you can:
1. Generate SD3 prompts from images
2. Enhance your text prompts (turn 1-2 words into full SD3 prompts)
https://huggingface.co/spaces/gokaygokay/SD3-with-VLM-and-Prompt-Enhancer
These features are based on my custom models:
- VLM captioner for prompt generation:
- https://huggingface.co/gokaygokay/sd3-long-captioner
- Prompt Enhancers for SD3 Models:
- https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long
- https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance
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๐ Arxiv: https://arxiv.org/abs/2407.01231
๐ Project page: https://mirai-llm.github.io
๐ป GitHub Repo: https://github.com/yecchen/MIRAI
๐ Dataset: https://drive.google.com/file/d/1xmSEHZ_wqtBu1AwLpJ8wCDYmT-jRpfrN/view?usp=sharing
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๐ง ๐ป Try it at my space: ๐ https://huggingface.co/spaces/awacke1/AnthropicClaude3.5Sonnet-ACW
Kudos to @AnthropicAI for this elegant API! ๐ #AI #CodeMagic #AnthropicAI Thanks Huggingface for hosting the best hub in the world for AI development!
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"value": "RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. ๐โจ",
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] | ๐ Transformers are not here to take part but take over... and down goes real-time object detection! ๐ฅ
Enter Real-time DEtection Transformer (RT-DETR) ๐ฆพ as suggested capable of real-time object detection. ๐ฏ
Object DEtection Transformer (DETR) is not new (@Meta did it eons ago) but it had the issue of every other transformer, high computational cost ๐ธ
RT-DETR brings an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed ๐๏ธ
Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. โก
It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. โ๏ธ
This makes RT-DETR faster and more accurate than previous YOLO models. ๐
How much better๐/faster? โฑ๏ธ
RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. ๐โจ
๐ Paper: https://huggingface.co/papers/2304.08069
๐ง Models: https://huggingface.co/models?search=pekingu/rt-detr | {
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] | 2024-07-01T22:32:05.000Z | 2024-07-01T22:32:05.701Z | [] | /posts/singhsidhukuldeep/260150398855068 | 1,536 | 0 |