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replied to their post 3 days ago
๐Ÿค— Hugging Face Download Tool The Hugging Face Download Tool is a sophisticated graphical user interface application designed to simplify the process of downloading resources from Hugging Face repositories. This tool addresses common challenges in model and file downloads through its intelligent features and user-friendly interface. โœจ Key Features - ๐Ÿ–ฅ๏ธ Intuitive graphical interface for easy operation - ๐Ÿ”„ Advanced retry mechanism with smart error handling - โธ๏ธ Resume capability for interrupted downloads - ๐Ÿ“Š Real-time download status monitoring - ๐Ÿ” Secure access to private repositories via token authentication ๐Ÿ› ๏ธ Technical Highlights The tool implements several advanced features to ensure reliable downloads: - ๐Ÿ“ฆ Chunk-based downloading with 1MB segments - โšก Adaptive retry intervals (5-300 seconds) based on error types - ๐Ÿ”Œ Connection pooling for optimized performance - ๐Ÿ›ก๏ธ Built-in rate limiting protection - ๐Ÿ”‘ Secure token handling for private repository access This tool is ideal for researchers, developers, and AI practitioners who regularly work with Hugging Face resources and need a reliable, user-friendly download solution. ๐Ÿ’ป It supports all major operating systems and requires minimal setup, making it accessible to users of all technical levels. ๐Ÿš€ GitHub๏ผšhttps://github.com/2404589803/hf_downloader
reacted to their post with ๐Ÿ˜Ž 4 days ago
๐Ÿค— Hugging Face Download Tool The Hugging Face Download Tool is a sophisticated graphical user interface application designed to simplify the process of downloading resources from Hugging Face repositories. This tool addresses common challenges in model and file downloads through its intelligent features and user-friendly interface. โœจ Key Features - ๐Ÿ–ฅ๏ธ Intuitive graphical interface for easy operation - ๐Ÿ”„ Advanced retry mechanism with smart error handling - โธ๏ธ Resume capability for interrupted downloads - ๐Ÿ“Š Real-time download status monitoring - ๐Ÿ” Secure access to private repositories via token authentication ๐Ÿ› ๏ธ Technical Highlights The tool implements several advanced features to ensure reliable downloads: - ๐Ÿ“ฆ Chunk-based downloading with 1MB segments - โšก Adaptive retry intervals (5-300 seconds) based on error types - ๐Ÿ”Œ Connection pooling for optimized performance - ๐Ÿ›ก๏ธ Built-in rate limiting protection - ๐Ÿ”‘ Secure token handling for private repository access This tool is ideal for researchers, developers, and AI practitioners who regularly work with Hugging Face resources and need a reliable, user-friendly download solution. ๐Ÿ’ป It supports all major operating systems and requires minimal setup, making it accessible to users of all technical levels. ๐Ÿš€ GitHub๏ผšhttps://github.com/2404589803/hf_downloader
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posted an update 3 days ago
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2422
๐ŸŽ‰ Update: HF Downloader now supports English!

๐ŸŒ We're excited to announce that HF Downloader now fully supports English interface!

โœจ What's New:
- Complete English UI
- Bilingual documentation
- Seamless language switching
- Real-time translation of download status

๐Ÿ” Whether you're downloading:
- Models
- Datasets
- Spaces

The interface will adapt to your language preference automatically.

๐Ÿš€ Try it now: Switch languages easily in the top-right corner of the app!

#HuggingFace #OpenSource #Update #GUI
replied to their post 3 days ago
reacted to their post with ๐Ÿ˜Ž 4 days ago
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2583
๐Ÿค— Hugging Face Download Tool

The Hugging Face Download Tool is a sophisticated graphical user interface application designed to simplify the process of downloading resources from Hugging Face repositories. This tool addresses common challenges in model and file downloads through its intelligent features and user-friendly interface.

โœจ Key Features
- ๐Ÿ–ฅ๏ธ Intuitive graphical interface for easy operation
- ๐Ÿ”„ Advanced retry mechanism with smart error handling
- โธ๏ธ Resume capability for interrupted downloads
- ๐Ÿ“Š Real-time download status monitoring
- ๐Ÿ” Secure access to private repositories via token authentication

๐Ÿ› ๏ธ Technical Highlights
The tool implements several advanced features to ensure reliable downloads:
- ๐Ÿ“ฆ Chunk-based downloading with 1MB segments
- โšก Adaptive retry intervals (5-300 seconds) based on error types
- ๐Ÿ”Œ Connection pooling for optimized performance
- ๐Ÿ›ก๏ธ Built-in rate limiting protection
- ๐Ÿ”‘ Secure token handling for private repository access

This tool is ideal for researchers, developers, and AI practitioners who regularly work with Hugging Face resources and need a reliable, user-friendly download solution. ๐Ÿ’ป It supports all major operating systems and requires minimal setup, making it accessible to users of all technical levels. ๐Ÿš€

GitHub๏ผšhttps://github.com/2404589803/hf_downloader
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posted an update 6 days ago
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Post
2583
๐Ÿค— Hugging Face Download Tool

The Hugging Face Download Tool is a sophisticated graphical user interface application designed to simplify the process of downloading resources from Hugging Face repositories. This tool addresses common challenges in model and file downloads through its intelligent features and user-friendly interface.

โœจ Key Features
- ๐Ÿ–ฅ๏ธ Intuitive graphical interface for easy operation
- ๐Ÿ”„ Advanced retry mechanism with smart error handling
- โธ๏ธ Resume capability for interrupted downloads
- ๐Ÿ“Š Real-time download status monitoring
- ๐Ÿ” Secure access to private repositories via token authentication

๐Ÿ› ๏ธ Technical Highlights
The tool implements several advanced features to ensure reliable downloads:
- ๐Ÿ“ฆ Chunk-based downloading with 1MB segments
- โšก Adaptive retry intervals (5-300 seconds) based on error types
- ๐Ÿ”Œ Connection pooling for optimized performance
- ๐Ÿ›ก๏ธ Built-in rate limiting protection
- ๐Ÿ”‘ Secure token handling for private repository access

This tool is ideal for researchers, developers, and AI practitioners who regularly work with Hugging Face resources and need a reliable, user-friendly download solution. ๐Ÿ’ป It supports all major operating systems and requires minimal setup, making it accessible to users of all technical levels. ๐Ÿš€

GitHub๏ผšhttps://github.com/2404589803/hf_downloader
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reacted to their post with ๐Ÿค 7 months ago
posted an update 7 months ago
reacted to julien-c's post with โค๏ธ 11 months ago
reacted to julien-c's post with ๐Ÿ‘๐Ÿค 12 months ago
reacted to akhaliq's post with ๐Ÿ‘ 12 months ago
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Self-Rewarding Language Models

paper page: Self-Rewarding Language Models (2401.10020)

Fine-tuning Llama 2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613
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reacted to gsarti's post with ๐Ÿ‘ 12 months ago
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๐Ÿ” Today's pick in Interpretability & Analysis of LMs: Can Large Language Models Explain Themselves? by @andreasmadsen Sarath Chandar & @sivareddyg

LLMs can provide wrong but convincing explanations for their behavior, and this might lead to ill-placed confidence in their predictions. This study uses self-consistency checks to measure the faithfulness of LLM explanations: if an LLM says a set of words is important for making a prediction, then it should not be able to make the same prediction without these words. Results demonstrate that LLM self-explanations faithfulness of self-explanations cannot be reliably trusted, as they prove to be very task and model dependent, with bigger model generally producing more faithful explanations.

๐Ÿ“„ Paper: Can Large Language Models Explain Themselves? (2401.07927)
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reacted to Linaqruf's post with โค๏ธ 12 months ago
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First post!
Very cool feature.
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reacted to dhuynh95's post with ๐Ÿ‘ 12 months ago
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๐ŸชŸ32k-context BERT for embedding and RAG on long corpus

Monarch Mixer is a new architecture to enable long context BERT for large corpus and can be fine-tuned for large context retrieval.

Quite interesting and important as BERT is still the most used LLM in production for "old school" tasks like classification, NER, embeddings, but is also a key component for RAG.

Paper: https://arxiv.org/abs/2310.12109
Blog: https://hazyresearch.stanford.edu/blog/2024-01-11-m2-bert-retrieval
GitHub: https://github.com/HazyResearch/m2
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