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ChunTe Lee

Chunte
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reacted to merve's post with 🚀 2 days ago
IBM released https://huggingface.co/ibm-granite/granite-vision-3.1-2b-preview, a small vision LM with impressive performance on different tasks 😮🔥 it comes with transformers and vLLM support from the get-go 💗 you can run it in Colab T4, so I built a notebook to put it to test, find it here: https://github.com/merveenoyan/smol-vision/blob/main/inference_gists/IBM_Granite_Vision.ipynb
reacted to albertvillanova's post with 🔥 2 days ago
🚀 Introducing @huggingface Open Deep-Research💥 In just 24 hours, we built an open-source agent that: ✅ Autonomously browse the web ✅ Search, scroll & extract info ✅ Download & manipulate files ✅ Run calculations on data 55% on GAIA validation set! Help us improve it!💡 https://huggingface.co/blog/open-deep-research
reacted to albertvillanova's post with 🤗 2 days ago
🚀 Introducing @huggingface Open Deep-Research💥 In just 24 hours, we built an open-source agent that: ✅ Autonomously browse the web ✅ Search, scroll & extract info ✅ Download & manipulate files ✅ Run calculations on data 55% on GAIA validation set! Help us improve it!💡 https://huggingface.co/blog/open-deep-research
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Chunte's activity

reacted to merve's post with 🚀 2 days ago
reacted to albertvillanova's post with 🔥🤗 2 days ago
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Post
2711
🚀 Introducing @huggingface Open Deep-Research💥

In just 24 hours, we built an open-source agent that:
✅ Autonomously browse the web
✅ Search, scroll & extract info
✅ Download & manipulate files
✅ Run calculations on data

55% on GAIA validation set! Help us improve it!💡
https://huggingface.co/blog/open-deep-research
  • 3 replies
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reacted to victor's post with 🤗🔥❤️ 3 days ago
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3650
Hey everyone, we've given https://hf.co/spaces page a fresh update!

Smart Search: Now just type what you want to do—like "make a viral meme" or "generate music"—and our search gets it.

New Categories: Check out the cool new filter bar with icons to help you pick a category fast.

Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.

Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.

We’d love to hear what you think—drop us some feedback plz!
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upvoted an article 8 days ago
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Article

The AI tools for Art Newsletter - Issue 1

• 48
upvoted an article 9 days ago
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Article

KV Caching Explained: Optimizing Transformer Inference Efficiency

By not-lain •
• 23
reacted to singhsidhukuldeep's post with ➕ 11 days ago
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2966
Exciting breakthrough in Retrieval-Augmented Generation (RAG): Introducing MiniRAG - a revolutionary approach that makes RAG systems accessible for edge devices and resource-constrained environments.

Key innovations that set MiniRAG apart:

Semantic-aware Heterogeneous Graph Indexing
- Combines text chunks and named entities in a unified structure
- Reduces reliance on complex semantic understanding
- Creates rich semantic networks for precise information retrieval

Lightweight Topology-Enhanced Retrieval
- Leverages graph structures for efficient knowledge discovery
- Uses pattern matching and localized text processing
- Implements query-guided reasoning path discovery

Impressive Performance Metrics
- Achieves comparable results to LLM-based methods while using Small Language Models (SLMs)
- Requires only 25% of storage space compared to existing solutions
- Maintains robust performance with accuracy reduction ranging from just 0.8% to 20%

The researchers from Hong Kong University have also contributed a comprehensive benchmark dataset specifically designed for evaluating lightweight RAG systems under realistic on-device scenarios.

This breakthrough opens new possibilities for:
- Edge device AI applications
- Privacy-sensitive implementations
- Real-time processing systems
- Resource-constrained environments

The full implementation and datasets are available on GitHub: HKUDS/MiniRAG
  • 1 reply
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reacted to sayakpaul's post with 🚀🤗 11 days ago
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1904
We have authored a post to go over the state of video generation in the Diffusers ecosystem 🧨

We cover the models supported, the knobs of optims our users can fire, fine-tuning, and more 🔥

5-6GBs for HunyuanVideo, sky is the limit 🌌 🤗
https://huggingface.co/blog/video_gen
liked a model 11 days ago
published a model 11 days ago
upvoted 5 articles 11 days ago
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Article

Run ComfyUI workflows for free on Spaces

• 51
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Article

Open-R1: a fully open reproduction of DeepSeek-R1

• 675
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Article

SmolVLM Grows Smaller – Introducing the 250M & 500M Models!

• 121
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State of open video generation models in Diffusers

• 31
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Welcome to Inference Providers on the Hub 🔥

• 290
updated a model 15 days ago
reacted to singhsidhukuldeep's post with 😔 17 days ago
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Post
2966
Exciting breakthrough in Retrieval-Augmented Generation (RAG): Introducing MiniRAG - a revolutionary approach that makes RAG systems accessible for edge devices and resource-constrained environments.

Key innovations that set MiniRAG apart:

Semantic-aware Heterogeneous Graph Indexing
- Combines text chunks and named entities in a unified structure
- Reduces reliance on complex semantic understanding
- Creates rich semantic networks for precise information retrieval

Lightweight Topology-Enhanced Retrieval
- Leverages graph structures for efficient knowledge discovery
- Uses pattern matching and localized text processing
- Implements query-guided reasoning path discovery

Impressive Performance Metrics
- Achieves comparable results to LLM-based methods while using Small Language Models (SLMs)
- Requires only 25% of storage space compared to existing solutions
- Maintains robust performance with accuracy reduction ranging from just 0.8% to 20%

The researchers from Hong Kong University have also contributed a comprehensive benchmark dataset specifically designed for evaluating lightweight RAG systems under realistic on-device scenarios.

This breakthrough opens new possibilities for:
- Edge device AI applications
- Privacy-sensitive implementations
- Real-time processing systems
- Resource-constrained environments

The full implementation and datasets are available on GitHub: HKUDS/MiniRAG
  • 1 reply
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