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v2ray/deepgelbooru
A Danbooru tag image tagger, maybe better than WD14 at some images.
Training code, inference code, dataset included.
:3
https://huggingface.co/v2ray/deepgelbooru
A Danbooru tag image tagger, maybe better than WD14 at some images.
Training code, inference code, dataset included.
:3
https://huggingface.co/v2ray/deepgelbooru
I can't believe this... Phi-3.5-mini (3.8B) running in-browser at ~90 tokens/second on WebGPU w/ Transformers.js and ONNX Runtime Web! 🤯 Since everything runs 100% locally, no messages are sent to a server — a huge win for privacy!
- 🤗 Demo:
webml-community/phi-3.5-webgpu
- 🧑💻 Source code: https://github.com/huggingface/transformers.js-examples/tree/main/phi-3.5-webgpu
- 🤗 Demo:
webml-community/phi-3.5-webgpu
- 🧑💻 Source code: https://github.com/huggingface/transformers.js-examples/tree/main/phi-3.5-webgpu
You can now use DoRA for your embedding layers!
PR: https://github.com/huggingface/peft/pull/2006
I have documented my journey of this specific PR in a blog post for everyone to read. The highlight of the PR was when the first author of DoRA reviewed my code.
Blog Post: https://huggingface.co/blog/ariG23498/peft-dora
Huge thanks to @BenjaminB for all the help I needed.
PR: https://github.com/huggingface/peft/pull/2006
I have documented my journey of this specific PR in a blog post for everyone to read. The highlight of the PR was when the first author of DoRA reviewed my code.
Blog Post: https://huggingface.co/blog/ariG23498/peft-dora
Huge thanks to @BenjaminB for all the help I needed.
🌐 Check out the new dataset sourced from Fishki.net, one of the popular entertainment and news portals in the Russian Internet, known for its diverse content including humor, interesting facts, and viral stories -
nyuuzyou/fishkinet-posts
.
📊 Dataset highlights:
- 369,180 posts
- Includes original posts with titles, content, images, and metadata
- Each entry contains URL, title, author, date, tags, content, and image URLs
- Primarily in Russian language
- Covers a wide range of topics in entertainment, news, and social media content
- Spans nearly two decades of posts, likely from early 2000s to 2024
- Dedicated to public domain under Creative Commons Zero (CC0) license
nyuuzyou/fishkinet-posts
.
📊 Dataset highlights:
- 369,180 posts
- Includes original posts with titles, content, images, and metadata
- Each entry contains URL, title, author, date, tags, content, and image URLs
- Primarily in Russian language
- Covers a wide range of topics in entertainment, news, and social media content
- Spans nearly two decades of posts, likely from early 2000s to 2024
- Dedicated to public domain under Creative Commons Zero (CC0) license
Alan Turing's mind-bender: "Can machines think?" in its glorified form. This 74yr old paper laid the foundation for how we think about AI and machine intelligence today. The level of detail, clarity and foresight is just phenomenal - he was way ahead of his time 🧠🤖
Original copy: https://archive.org/details/MIND--COMPUTING-MACHINERY-AND-INTELLIGENCE
Original copy: https://archive.org/details/MIND--COMPUTING-MACHINERY-AND-INTELLIGENCE
g/ - /ldg/ - Local Diffusion General - Technology
>Beginner UI
EasyDiffusion: https://easydiffusion.github.io
Fooocus: https://github.com/lllyasviel/fooocus
Metastable: https://metastable.studio
>Advanced UI
Automatic1111: https://github.com/automatic1111/stable-diffusion-webui
ComfyUI: https://github.com/comfyanonymous/ComfyUI
InvokeAI: https://github.com/invoke-ai/InvokeAI
SD.Next: https://github.com/vladmandic/automatic
SwarmUI: https://github.com/mcmonkeyprojects/SwarmUI
>Beginner UI
EasyDiffusion: https://easydiffusion.github.io
Fooocus: https://github.com/lllyasviel/fooocus
Metastable: https://metastable.studio
>Advanced UI
Automatic1111: https://github.com/automatic1111/stable-diffusion-webui
ComfyUI: https://github.com/comfyanonymous/ComfyUI
InvokeAI: https://github.com/invoke-ai/InvokeAI
SD.Next: https://github.com/vladmandic/automatic
SwarmUI: https://github.com/mcmonkeyprojects/SwarmUI
Introducing HelpingAI2-9B, an emotionally intelligent LLM.
Model Link :
OEvortex/HelpingAI2-9B
Demo Link:
Abhaykoul/HelpingAI2
This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.
Key Features:
-----------------
* It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.
* It gives responses in empathetic and supportive manner.
Must try our demo:
Abhaykoul/HelpingAI2
Model Link :
OEvortex/HelpingAI2-9B
Demo Link:
Abhaykoul/HelpingAI2
This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.
Key Features:
-----------------
* It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.
* It gives responses in empathetic and supportive manner.
Must try our demo:
Abhaykoul/HelpingAI2
NEW math-instruct model + dataset!
ValiantLabs/Llama3.1-8B-Cobalt
is our new math-instruct model.
Trained using a synthetic math-instruct dataset generated with Llama 3.1 405b. Find the dataset here:
sequelbox/Polytope
More to come soon :)
ValiantLabs/Llama3.1-8B-Cobalt
is our new math-instruct model.
Trained using a synthetic math-instruct dataset generated with Llama 3.1 405b. Find the dataset here:
sequelbox/Polytope
More to come soon :)
Supercool Weekend Read🤖
Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.
Details on Techniques (Simplified):
They started off with a large pre-trained language model (15B params), then:
1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.
Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.
Details on Techniques (Simplified):
They started off with a large pre-trained language model (15B params), then:
1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.
What Happens When RAG System Become Fully Vision-Language Model-Based?
HF Demo:
bokesyo/MiniCPMV-RAG-PDFQA
Multimodal Dense Retriever:
RhapsodyAI/minicpm-visual-embedding-v0
Generation Model:
openbmb/MiniCPM-V-2_6
Github: https://github.com/RhapsodyAILab/MiniCPM-V-Embedding-v0-Train
The Vision-Language Model Dense Retriever MiniCPM-Visual-Embedding-v0 reads PDFs directly -- no OCR required. With strong OCR capability and visual understanding capability, it generates multimodal dense representations, allowing you to build and search through your personal library with ease.
Ask a question, it retrieves the most relevant pages. Then, MiniCPM-V-2.6 provides answers based on the retrieved pages, with strong multi-image understanding capabilities.
Whether you’re working with a visually-intensive or text-oriented PDF, it helps you quickly find the information you need. You can also build a personal library with it.
It operates just like a human: reading, storing, retrieving, and answering with full visual comprehension.
Currently, the online demo supports PDFs with up to 50 pages due to GPU time limits. For longer PDFs or entire books, you can deploy it on your own machine.
https://cdn-uploads.huggingface.co/production/uploads/6415818a986557e8cac252bf/sjtQD7CFgox46h9EVHCG_.png
HF Demo:
bokesyo/MiniCPMV-RAG-PDFQA
Multimodal Dense Retriever:
RhapsodyAI/minicpm-visual-embedding-v0
Generation Model:
openbmb/MiniCPM-V-2_6
Github: https://github.com/RhapsodyAILab/MiniCPM-V-Embedding-v0-Train
The Vision-Language Model Dense Retriever MiniCPM-Visual-Embedding-v0 reads PDFs directly -- no OCR required. With strong OCR capability and visual understanding capability, it generates multimodal dense representations, allowing you to build and search through your personal library with ease.
Ask a question, it retrieves the most relevant pages. Then, MiniCPM-V-2.6 provides answers based on the retrieved pages, with strong multi-image understanding capabilities.
Whether you’re working with a visually-intensive or text-oriented PDF, it helps you quickly find the information you need. You can also build a personal library with it.
It operates just like a human: reading, storing, retrieving, and answering with full visual comprehension.
Currently, the online demo supports PDFs with up to 50 pages due to GPU time limits. For longer PDFs or entire books, you can deploy it on your own machine.
https://cdn-uploads.huggingface.co/production/uploads/6415818a986557e8cac252bf/sjtQD7CFgox46h9EVHCG_.png
So turns out I've been spreading a bit of misinformation when it comes to imatrix in llama.cpp
It starts true; imatrix runs the model against a corpus of text and tracks the activation of weights to determine which are most important
However what the quantization then does with that information is where I was wrong.
I think I made the accidental connection between imatrix and exllamav2's measuring, where ExLlamaV2 decides how many bits to assign to which weight depending on the goal BPW
Instead, what llama.cpp with imatrix does is it attempts to select a scale for a quantization block that most accurately returns the important weights to their original values, ie minimizing the dequantization error based on the importance of activations
The mildly surprising part is that it actually just does a relatively brute force search, it picks a bunch of scales and tries each and sees which one results in the minimum error for weights deemed important in the group
But yeah, turns out, the quantization scheme is always the same, it's just that the scaling has a bit more logic to it when you use imatrix
Huge shoutout to @compilade for helping me wrap my head around it - feel free to add/correct as well if I've messed something up
It starts true; imatrix runs the model against a corpus of text and tracks the activation of weights to determine which are most important
However what the quantization then does with that information is where I was wrong.
I think I made the accidental connection between imatrix and exllamav2's measuring, where ExLlamaV2 decides how many bits to assign to which weight depending on the goal BPW
Instead, what llama.cpp with imatrix does is it attempts to select a scale for a quantization block that most accurately returns the important weights to their original values, ie minimizing the dequantization error based on the importance of activations
The mildly surprising part is that it actually just does a relatively brute force search, it picks a bunch of scales and tries each and sees which one results in the minimum error for weights deemed important in the group
But yeah, turns out, the quantization scheme is always the same, it's just that the scaling has a bit more logic to it when you use imatrix
Huge shoutout to @compilade for helping me wrap my head around it - feel free to add/correct as well if I've messed something up
How good are you at spotting AI-generated images?
Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments!
victor/fake-insects
Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments!
victor/fake-insects
I'm excited to share a really cool milestone in my AI/LLM journey.
Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices.
Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!
Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices.
Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!
🚀 We’re excited to launch Ghost 8B Beta (1608), a top-performing language model with unmatched multilingual support and cost efficiency.
Key Highlights:
- Superior Performance: Outperforms Llama 3.1 8B Instruct, GPT-3.5 Turbo, Claude 3 Opus, GPT-4, and more in winrate scores.
- Expanded Language Support: Now supports 16 languages, including English, Vietnamese, Spanish, Chinese, and more.
- Enhanced Capabilities: Improved math, reasoning, and instruction-following for better task handling.
Key Highlights:
- Superior Performance: Outperforms Llama 3.1 8B Instruct, GPT-3.5 Turbo, Claude 3 Opus, GPT-4, and more in winrate scores.
- Expanded Language Support: Now supports 16 languages, including English, Vietnamese, Spanish, Chinese, and more.
- Enhanced Capabilities: Improved math, reasoning, and instruction-following for better task handling.
🔔 Release: small-text v1.4.1
The new release contains some smaller bugfixes. Check it out!
Github: https://github.com/webis-de/small-text
Paper:
Small-Text: Active Learning for Text Classification in Python (2107.10314)
The new release contains some smaller bugfixes. Check it out!
Github: https://github.com/webis-de/small-text
Paper:
Small-Text: Active Learning for Text Classification in Python (2107.10314)
Put together a small repo showing how to go from making your own fine-tuning dataset w/ services like Groq & Together to publishing that model on ollama.
In my case I fine-tuned SmolLM-360M to be a better assistant for my Pi-Card (previous post) project.
Check it out!
https://github.com/nkasmanoff/ft-flow
In my case I fine-tuned SmolLM-360M to be a better assistant for my Pi-Card (previous post) project.
Check it out!
https://github.com/nkasmanoff/ft-flow
ResShift 1-Click Windows, RunPod, Massed Compute, Kaggle Installers with Amazing Gradio APP and Batch Image Processing. ResShift is Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS 2023, Spotlight).
Official Repo : https://github.com/zsyOAOA/ResShift
I have developed a very advanced Gradio APP.
Official Repo : https://github.com/zsyOAOA/ResShift
I have developed a very advanced Gradio APP.
🚀 Introducing Hugging Face Similar: a Chrome extension to find relevant datasets!
✨ Adds a "Similar Datasets" section to Hugging Face dataset pages
🔍 Recommendations based on dataset READMEs
🏗 Powered by https://huggingface.co/chromadb and https://huggingface.co/Snowflake embeddings.
You can try it here: https://chromewebstore.google.com/detail/hugging-face-similar/aijelnjllajooinkcpkpbhckbghghpnl?authuser=0&hl=en.
I am very happy to get feedback on whether this could be useful or not 🤗
✨ Adds a "Similar Datasets" section to Hugging Face dataset pages
🔍 Recommendations based on dataset READMEs
🏗 Powered by https://huggingface.co/chromadb and https://huggingface.co/Snowflake embeddings.
You can try it here: https://chromewebstore.google.com/detail/hugging-face-similar/aijelnjllajooinkcpkpbhckbghghpnl?authuser=0&hl=en.
I am very happy to get feedback on whether this could be useful or not 🤗
🤗 Serving Meta Llama 3.1 405B on Google Cloud is now possible via the Hugging Face Deep Learning Containers (DLCs) for Text Generation Inference (TGI)
In this post, we showcase how to deploy
meta-llama/Meta-Llama-3.1-405B-Instruct-FP8
on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as
In this post, we showcase how to deploy
meta-llama/Meta-Llama-3.1-405B-Instruct-FP8
on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as
🚀 How The Washington Post Uses AI to Empower Journalists 🔍📰
An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:
🎥 What it does:
• Extracts stills from video files
• Processes on-screen text
An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:
🎥 What it does:
• Extracts stills from video files
• Processes on-screen text