Adam Molnar

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reacted to merve's post with 👀🚀❤️🔥 about 16 hours ago

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Aya by Cohere For AI can now see! 👀

C4AI community has built Maya 8B, a new open-source multilingual VLM built on SigLIP and Aya 8B 🌱 works on 8 languages! 🗣️

The authors extend Llava dataset using Aya's translation capabilities with 558k examples!
ry it here kkr5155/maya_demo

Dataset maya-multimodal/pretrain

Model maya-multimodal/maya 👏
kudos @nahidalam and team

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replied to nyuuzyou's post 6 days ago

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Unrelated but wanted to check re: spam stuff, is this your account btw? (I assumed it was an impersonator/troll but feel free to correct me)

reacted to lewtun's post with 👍🚀👀❤️🔥 8 days ago

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We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥

How? By combining step-wise reward models with tree search algorithms :)

We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"

We're open sourcing the full recipe and sharing a detailed blog post.

In our blog post we cover:

📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.

🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.

🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM

Here's the links:

- Blog post: HuggingFaceH4/blogpost-scaling-test-time-compute

- Code: https://github.com/huggingface/search-and-learn

Enjoy!

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reacted to lorraine2's post with 🚀 8 days ago

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🦙New NVIDIA paper: LLaMA-Mesh 🦙

We enable large language models to generate and understand 3D meshes by representing them as text and fine-tuning. This unifies the 3D and text modalities in a single model and preserves language abilities, unlocking conversational 3D creation with mesh understanding.

🔎 Project Page: https://research.nvidia.com/labs/toronto-ai/LLaMA-Mesh/
🕹️ Interactive Demo: Zhengyi/LLaMA-Mesh (courtesy of HuggingFace and Gradio)
📖 Full Paper: https://arxiv.org/abs/2411.09595
👨‍💻Code: https://github.com/nv-tlabs/LLaMa-Mesh
💾 Model Checkpoint: Zhengyi/LLaMA-Mesh
🧩 Blender Addon: https://github.com/huggingface/meshgen (courtesy of Dylan Ebert)
🎥 5-min Overview Video: https://youtu.be/eZNazN-1lPo?si=-idQa5aaceVw0Bbj (courtesy of AI Papers Academy)

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reacted to YerbaPage's post with 👀 8 days ago

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Curated list of **Repository-level Code Generation** papers & benchmarks! 🔥

Stay ahead with the latest in:
✅ Repo-level Issue Resolution
✅ Repo-level Code Completion
✅ Datasets & Benchmarks

👉 Check it out: https://github.com/YerbaPage/Awesome-Repo-Level-Code-Generation 🔥

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reacted to wenhuach's post with 🔥👀 8 days ago

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AutoRound has demonstrated strong results even at 2-bit precision for VLM models like QWEN2-VL-72B. Check it out here: OPEA/Qwen2-VL-72B-Instruct-int2-sym-inc.

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reacted to cutechicken's post with 🤝🔥➕❤️🚀 8 days ago

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🚀 RAGOndevice: High-Performance Local AI Document Analysis Assistant
💫 Core Value
RAGOndevice is a high-performance AI system running locally without cloud dependency. Using CohereForAI's optimized 7B model, it enables professional-grade document analysis on standard PCs. ✨
🌟 Ondevice AI Advantages
1. 🔋 Efficient Resource Utilization

🎯 Optimized 7B Model: Runs on standard PCs
⚡ Local Processing: Instant response without cloud
💻 Low-Spec Compatible: Performs well on regular GPUs
🔄 Optimized Memory: Ensures stable operation

2. 🛡️ Data Security & Cost Efficiency

🔒 Complete Privacy: No external data transmission
🌐 Offline Operation: No internet required
💰 No Subscription: One-time installation
⚙️ Resource Optimization: Uses existing hardware

🎮 Key Features
1. 📊 Powerful Document Analysis

📁 Multi-Format Support: TXT, CSV, PDF, Parquet
🧠 Intelligent Analysis: Automatic structure recognition
👁️ OCR Support: Advanced PDF text extraction
💬 Real-time Chat: Natural language interaction

2. 🔍 Local RAG System

🎯 Efficient Search: TF-IDF based local search
🧩 Context Understanding: Accurate information retrieval
📚 Wikipedia Integration: Rich background knowledge

🎯 Use Cases

🏢 Enterprise: Secure confidential document processing
🔬 Personal Research: Private data analysis
📚 Education: Personal learning material analysis
💻 Development: Local codebase analysis

⭐ Differentiators

🏃‍♂️ Independent Operation: Zero cloud dependency
⚡ Instant Response: No network latency
🔐 Complete Security: Full data control
💎 Cost Efficiency: No ongoing costs

🔮 Future Plans

🚀 Enhanced model optimization
📚 Local knowledge base expansion
⚡ Hardware optimization
📁 Extended file support


🌟 RAGOndevice democratizes high-performance AI, providing the optimal local AI solution for security-sensitive environments. 🚀

🔥 Power of Local AI: Experience enterprise-grade AI capabilities right on your device!

VIDraft/RAGOndevice

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reacted to nataliaElv's post with ❤️ 8 days ago

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If you are still wondering how the FineWeb2 annotations are done, how to follow the guidelines or how Argilla works, this is your video!

I go through a few samples of the FineWeb2 dataset and classify them based on their educational content. Check it out!

https://www.youtube.com/watch?v=_-ORB4WAVGU

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