John6666 (John Smith)

reacted to neph1's post with 👀 2 minutes ago

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There's a new version of the Swedish instruct model, bellman. Due to 'popular demand' (at least as opposed to 'no demand'), I based it off the latest mistral 7b, v0.3. The v0.2 seems to be the most popular of the bunch, despite being quite old by now. Why, I don't know. Must be a link in some old reddit post that is drawing clicks. :)
Anyway, here it is:
neph1/bellman-mistral-7b-instruct-v0.3
You can also try it out (on cpu), here:
neph1/bellman

reacted to singhsidhukuldeep's post with 🔥 2 minutes ago

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Exciting breakthrough in large-scale recommendation systems! ByteDance researchers have developed a novel real-time indexing method called "Streaming Vector Quantization" (Streaming VQ) that revolutionizes how recommendations work at scale.

>> Key Innovations

Real-time Indexing: Unlike traditional methods that require periodic reconstruction of indexes, Streaming VQ attaches items to clusters in real time, enabling immediate capture of emerging trends and user interests.

Superior Balance: The system achieves remarkable index balancing through innovative techniques like merge-sort modification and popularity-aware cluster assignment, ensuring all clusters participate effectively in recommendations.

Implementation Efficiency: Built on VQ-VAE architecture, Streaming VQ features a lightweight and clear framework that makes it highly implementation-friendly for large-scale deployments.

>> Technical Deep Dive

The system operates in two key stages:
- An indexing step using a two-tower architecture for real-time item-cluster assignment
- A ranking step that employs sophisticated attention mechanisms and deep neural networks for precise recommendations.

>> Real-world Impact

Already deployed in Douyin and Douyin Lite, replacing all major retrievers and delivering significant user engagement improvements. The system handles a billion-scale corpus while maintaining exceptional performance and computational efficiency.

This represents a significant leap forward in recommendation system architecture, especially for platforms dealing with dynamic, rapidly-evolving content. The ByteDance team's work demonstrates how rethinking fundamental indexing approaches can lead to substantial real-world improvements.

reacted to Kseniase's post with 👀 about 5 hours ago

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10 Recent Advancements in Math Reasoning

Over the last few weeks, we have witnessed a surge in AI models' math reasoning capabilities. Top companies like Microsoft, NVIDIA, and Alibaba Qwen have already joined this race to make models "smarter" in mathematics. But why is this shift happening now?

Complex math calculations require advanced multi-step reasoning, making mathematics an ideal domain for demonstrating a model's strong "thinking" capabilities. Additionally, as AI continues to evolve and is applied in math-intensive fields such as machine learning and quantum computing (which is predicted to see significant growth in 2025), it must meet the demands of complex reasoning.
Moreover, AI models can be integrated with external tools like symbolic solvers or computational engines to tackle large-scale math problems, which also needs high-quality math reasoning.

So here’s a list of 10 recent advancements in math reasoning of AI models:

1. NVIDIA: AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling (2412.15084)

2. Qwen, Alibaba: Qwen2.5-Math The Lessons of Developing Process Reward Models in Mathematical Reasoning (2501.07301) and PROCESSBENCH evaluation ProcessBench: Identifying Process Errors in Mathematical Reasoning (2412.06559)

3. Microsoft Research: rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking (2501.04519)

4. BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning (2501.03226)

5. URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics (2501.04686)

6. U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs (2412.03205)

7. Open Eyes, Then Reason: Fine-grained Visual Mathematical Understanding in MLLMs (2501.06430)

8. End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach (2501.04425)

9. Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning (2501.03035)

10. System-2 Mathematical Reasoning via Enriched Instruction Tuning (2412.16964)

reacted to kadirnar's post with 🔥 about 5 hours ago

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I created my own AI image and video from scratch using the fal.ai platform 💫

Workflow: Flux Lora Training + Upscale + Kling AI(1.6)

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replied to nroggendorff's post about 7 hours ago

I beat Noa.😎

Attached GPUs                             : 1
GPU 00000000:06:00.0
    Clocks
        Graphics                          : 210 MHz
        SM                                : 210 MHz
        Memory                            : 405 MHz
        Video                             : 555 MHz

reacted to nicolay-r's post with 👍 about 7 hours ago

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📢 So far I been passioned about making NLP pipeline for handling iterator of texts with no-string dependency from besides third-party providers of your choice.

By starting with text-translation, delighted to share the related notebooks that might save you time for handling your data

⭐ https://github.com/nicolay-r/nlp-thirdgate

Example of using GoogleTranslate API in no-string for handling textual data iterators with spans:

📙 https://github.com/nicolay-r/nlp-thirdgate/blob/master/tutorials/translate_texts_with_spans_via_googletrans.ipynb

The key concept is that all these API examples could be tied into a single pipeline using AREkit

📘 https://github.com/nicolay-r/AREkit

🛠️ The further plan is to popualte this repo with
1. NER (DeepPavlov models wrapper)
2. LLM with fancy out-of-the-box chain-of-thought declaration support.

reacted to openfree's post with 🔥 about 8 hours ago

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🌟 Creating Presidential Images with FLUX: A Guide 🇰🇷
Model Link: openfree/korea-president-yoon
Service Link: openfree/korea-president-yoon

Hello! Today we'll explore how to generate presidential images using the FLUX model!
📱 Basic Settings

Base Model: black-forest-labs/FLUX.1-dev
LoRA: korea-president-yoon
License: flux-1-dev-non-commercial-license

🎨 How to Use

Always include 'president yoon' in your prompts
Various scenarios available:

In a cafe setting
As a soldier
Participating in a marathon, etc.

💻 Running the Code
pythonCopyfrom diffusers import AutoPipelineForText2Image
import torch

# Load FLUX model
pipeline = AutoPipelineForText2Image.from_pretrained(
'black-forest-labs/FLUX.1-dev',
torch_dtype=torch.bfloat16
).to('cuda')

# Apply LoRA weights
pipeline.load_lora_weights(
'openfree/korea-president-yoon',
weight_name='korea-president-yoon.safetensors'
)

# Generate and save image
image = pipeline('A person in a bustling cafe president yoon').images[0]
image.save("my_image.png")
🔧 Compatible Tools

ComfyUI
AUTOMATIC1111
SD.Next
Invoke AI

✨ Tips and Notes

Available in Safetensors format
Download from Files & versions tab
For non-commercial use only

For more details, please refer to the Hugging Face documentation! Happy image generation! 🎉

replied to DonImages's post about 8 hours ago

@meganariley Payment issue.

reacted to luigi12345's post with 🚀 about 8 hours ago

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🤔Create Beautiful Diagrams with FLUX WITHOUT DISTORTED TEXT✌️

from huggingface_hub import InferenceClient
client = InferenceClient("black-forest-labs/FLUX.1-schnell", token="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")

https://huggingface.co/spaces/black-forest-labs/FLUX.1-schnell
# output is a PIL.Image object
image = client.text_to_image("A handrawn colorful mind map diagram, rugosity drawn  lines, clear shapes, brain silhouette, text areas. must include the texts LITERACY/MENTAL ├── PEACE [Dove Icon] ├── HEALTH [Vitruvian Man ~60px] ├── CONNECT [Brain-Mind Connection Icon] ├── INTELLIGENCE │   └── EVERYTHING [Globe Icon ~50px] └── MEMORY     ├── READING [Book Icon ~40px]     ├── SPEED [Speedometer Icon]     └── CREATIVITY         └── INTELLIGENCE [Lightbulb + Infinity ~30px]")

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reacted to aiqcamp's post with 🔥 about 8 hours ago

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# 🎨 FLUX Diagram Generator - Create Hand-Drawn Style Diagrams

aiqcamp/diagram

Generate beautiful mind maps and diagrams with AI! Using the FLUX.1-schnell model, create natural hand-drawn style diagrams that bring your ideas to life.

## ✨ Key Features

- 💡 Intuitive prompt-based input system
- 🎯 Rich examples including knowledge trees, digital transformation, creative process, and more
- 🛠 Customizable settings for image size, seed values, and more
- 🖼 Support for resolutions up to 2048x2048
- ⚡ Fast generation (4 steps default)

## 🎯 Use Cases

- Educational materials
- Project planning
- Idea structuring
- Presentation visuals
- Business process visualization

Built with Gradio for a user-friendly interface that anyone can use. Start creating your own diagrams now! 🚀

Try it out to transform your ideas into visually appealing diagrams with a unique hand-drawn aesthetic.

#AIart #Diagram #Mindmap #Visualization #HuggingFace

1 reply

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reacted to lamhieu's post with 👍 about 8 hours ago

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Power Up RAG, Virtual Assistants, and Perplexity Alternatives! 🚀

🔗 Docsifer + Lightweight Embeddings API = The perfect duo for next-gen solutions!

- 📄 Docsifer: Seamlessly convert PDFs, Word, JSON, and URLs to Markdown—ideal for building clean, structured knowledge bases.
- ✨ Lightweight Embeddings API: Create multilingual and multimodal embeddings for fast, accurate search, reranking, and understanding.

🤖 Build smarter RAG pipelines, enhance virtual assistants, or craft powerful Perplexity-like applications with this free, production-ready combo.

👉 Start optimizing today:
- Docsifer: lamhieu/docsifer
- Lightweight Embeddings API: lamhieu/lightweight-embeddings

💡 Faster insights. Better recommendations. Global reach. 🚀

1 reply

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reacted to AlexBodner's post with 👀 about 18 hours ago

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I just dropped a detailed guide on deploying ML models to Google Cloud Run with GPU support—completely serverless and auto-scaling. If you’re curious about seamlessly deploying your models to the cloud, give it a read! [https://medium.com/@alexbodner/deployment-of-serverless-machine-learning-models-with-gpus-using-google-cloud-cloud-run-573b836475b5]"

1 reply

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reacted to ZennyKenny's post with 👀 about 18 hours ago

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Really pleased with the Bring Your Own Model (BYOM) feature in Brave Browser: https://brave.com/blog/byom-nightly/

Takes about 5 minutes to configure your own locally running LLM as an in-browser assistant. Totally local, totally private, totally yours.

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reacted to nroggendorff's post with 👀 about 18 hours ago

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I just found out my GPU clock speed is 130MHz.

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reacted to rwightman's post with 👍 1 day ago

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I re-worked the JuptyerLab Space template recently. It's optimized for timm use, but will work great with transformers and other libs. Updated the base image, Python 3.12, Pillow-SIMD before better CPU use with image preprocessing, and made a number of other tweaks. From the Jupyter launcher you can run the terminal and setup a timm environment in moments with setup_timm_dev or setup_timm_scripts helpers. Give it a try, timm/jupyterlab-timm

reacted to mkurman's post with 👍 1 day ago

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ReasonFlow 🧠

Are you fascinated by reasoning models? If so, you won't want to miss my latest project! I've implemented multiple path generations to supercharge the reasoning capabilities of O1-like models. Explore how this work can elevate your model in complex reasoning tasks!

https://github.com/mkurman/ReasonFlow

Use it with:
mkurman/phi4-MedIT-10B-o1
- or -
mkurman/llama-3.2-MEDIT-3B-o1

1 reply

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reacted to cutechicken's post with 🔥 1 day ago

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🔬 PaperImpact
: Scientific Impact Predictor Powered by Deep Learning 🎯

VIDraft/PaperImpact

📚 Overview
A cutting-edge AI system that combines transformer architecture with citation pattern analysis to predict research impact. Our model, trained on 120,000+ CS papers, analyzes innovation potential, methodological robustness, and future impact, providing researchers with valuable insights before publication.
🧠 Scientific Foundation

BERT-based semantic analysis
Citation network pattern learning
NDCG optimization & MSE loss
Cross-validated prediction engine
GPU-accelerated inference

💫 Why Researchers Need This

Pre-submission impact assessment
Research direction optimization
Time-saving paper evaluation
Competitive edge in academia
Trend identification advantage

🎯 Key Features

One-click arXiv paper analysis
Real-time impact scoring (0-1)
9-tier grading system (AAA-C)
Smart input validation
Instant visual feedback

🌟 Unique Benefits
"Don't wait years to know your paper's impact. Get instant, AI-powered insights to strengthen your research strategy and maximize your academic influence."
Perfect for:

Research authors
PhD students
Journal editors
Research institutions
Grant committees

#ResearchImpact #AcademicAI #ScienceMetrics #ResearchExcellence

1 reply

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reacted to nyuuzyou's post with ❤️ 1 day ago

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🎨 Artfol Dataset - nyuuzyou/artfol

A collection of 1,892,816 artwork posts featuring:
- High-quality art pieces with various styles and techniques
- Complete metadata including artist IDs, titles, and moderation flags
- Content from Artfol social media platform

The dataset contains:
- Public domain artwork posts
- Artist attribution and identifiers
- Direct image URLs and web page links
- Content safety flags (NSFW, gore)
- Post titles and descriptions

All content is available under CC0 license, allowing unrestricted use including commercial applications.

reacted to etemiz's post with 👀 1 day ago

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Updated the Hoopoe model which is taking faith related and religious texts in.

etemiz/Hoopoe-8B-Llama-3.1

Faith score went from 8% to 54%. Expect more updates and increase in the score. I also did the instruct fine tuning before adding faith to the model. So some of the improvements may be there because I started with llama 3.1 base and not the instruct.

Here are some comparisons with original Llama 3.1:

reacted to singhsidhukuldeep's post with 👀 1 day ago

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Exciting breakthrough in AI recommendation systems! A team of researchers from Meta, UMN, NCSU, and UNC Chapel Hill have developed an innovative framework that significantly improves both efficiency and accuracy of LLM-based recommender systems.

The framework introduces two key innovations:

>> GCN-Retriever
Their solution uses Graph Convolutional Networks (GCNs) to efficiently identify similar users by analyzing interaction patterns in user-item graphs. This replaces traditional LLM-based retrieval methods, dramatically reducing computational overhead while maintaining recommendation quality.

>> Multi-Head Early Exit Architecture
The system implements a novel early exit strategy with multiple prediction heads at different layers. By monitoring prediction confidence in real-time, the model can terminate processing early when sufficient confidence is reached, significantly improving inference speed.

>> Performance Highlights
- Achieved 96.37 AUC on Amazon Beauty dataset
- Up to 4.96x improvement in requests per second
- Maintains or improves accuracy while reducing computation time
- Successfully handles both sparse and dense interaction data

The framework addresses two critical bottlenecks in current LLM recommender systems: retrieval delays and inference slowdown. By combining GCN-based retrieval with dynamic early exit strategies, the system delivers faster, more accurate recommendations at scale.

This work represents a significant step forward in making LLM-based recommendation systems practical for real-world commercial applications. The framework's ability to balance efficiency and accuracy while maintaining robust performance across different datasets demonstrates its potential for wide-scale adoption.

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