nyuuzyou/klingai
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nyuuzyou/klingai
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nyuuzyou's
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10 days ago
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2195
🎨 KLING AI Dataset -
nyuuzyou/klingai
A collection of 12,782 AI-generated media items featuring:
- High-quality image and video generations at various resolutions
- Complete metadata including user IDs, prompts, and generation parameters
- Content generated using text-to-image, text-to-video, and image-to-video modalities
- Full generation settings and technical parameters
A collection of 12,782 AI-generated media items featuring:
- High-quality image and video generations at various resolutions
- Complete metadata including user IDs, prompts, and generation parameters
- Content generated using text-to-image, text-to-video, and image-to-video modalities
- Full generation settings and technical parameters
reacted to
csabakecskemeti's
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10 days ago
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2104
The
deepseek-ai/DeepSeek-V3-Base
model has featured today on CNBC tech news. The whale made a splash by using FP8 and shrink the cost of training significantly!
https://youtu.be/NJljq429cGk?si=kgk-ogPTMfJKsaA2
model has featured today on CNBC tech news. The whale made a splash by using FP8 and shrink the cost of training significantly!
https://youtu.be/NJljq429cGk?si=kgk-ogPTMfJKsaA2
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reacted to
roseking's
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10 days ago
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2622
🤗 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.
✨ Key Features
- 🖥️ Intuitive graphical interface for easy operation
- 🔄 Advanced retry mechanism with smart error handling
- ⏸️ Resume capability for interrupted downloads
- 📊 Real-time download status monitoring
- 🔐 Secure access to private repositories via token authentication
🛠️ Technical Highlights
The tool implements several advanced features to ensure reliable downloads:
- 📦 Chunk-based downloading with 1MB segments
- ⚡ Adaptive retry intervals (5-300 seconds) based on error types
- 🔌 Connection pooling for optimized performance
- 🛡️ Built-in rate limiting protection
- 🔑 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. 💻 It supports all major operating systems and requires minimal setup, making it accessible to users of all technical levels. 🚀
GitHub：https://github.com/2404589803/hf_downloader
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.
✨ Key Features
- 🖥️ Intuitive graphical interface for easy operation
- 🔄 Advanced retry mechanism with smart error handling
- ⏸️ Resume capability for interrupted downloads
- 📊 Real-time download status monitoring
- 🔐 Secure access to private repositories via token authentication
🛠️ Technical Highlights
The tool implements several advanced features to ensure reliable downloads:
- 📦 Chunk-based downloading with 1MB segments
- ⚡ Adaptive retry intervals (5-300 seconds) based on error types
- 🔌 Connection pooling for optimized performance
- 🛡️ Built-in rate limiting protection
- 🔑 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. 💻 It supports all major operating systems and requires minimal setup, making it accessible to users of all technical levels. 🚀
GitHub：https://github.com/2404589803/hf_downloader
- 3 replies
liked
3
models
10 days ago
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prithivMLmods's
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10 days ago
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2527
Qwen2VL Models: Vision and Language Processing 🍉
📍FT; [ Latex OCR, Math Parsing, Text Analogy OCRTest ]
Colab Demo: prithivMLmods/Qwen2-VL-OCR-2B-Instruct
❄️Demo : prithivMLmods/Qwen2-VL-2B . The demo includes the Qwen2VL 2B Base Model.
🎯The space handles documenting content from the input image along with standardized plain text. It includes adjustment tools with over 30 font styles, file formatting support for PDF and DOCX, textual alignments, font size adjustments, and line spacing modifications.
📄PDFs are rendered using the ReportLab software library toolkit.
🧵Models :
+ prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+ prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct
+ prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct
🚀Sample Document :
+ https://drive.google.com/file/d/1Hfqqzq4Xc-3eTjbz-jcQY84V5E1YM71E/view?usp=sharing
📦Collection :
+ prithivMLmods/vision-language-models-67639f790e806e1f9799979f
.
.
.
@prithivMLmods 🤗
📍FT; [ Latex OCR, Math Parsing, Text Analogy OCRTest ]
Colab Demo: prithivMLmods/Qwen2-VL-OCR-2B-Instruct
❄️Demo : prithivMLmods/Qwen2-VL-2B . The demo includes the Qwen2VL 2B Base Model.
🎯The space handles documenting content from the input image along with standardized plain text. It includes adjustment tools with over 30 font styles, file formatting support for PDF and DOCX, textual alignments, font size adjustments, and line spacing modifications.
📄PDFs are rendered using the ReportLab software library toolkit.
🧵Models :
+ prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+ prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct
+ prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct
🚀Sample Document :
+ https://drive.google.com/file/d/1Hfqqzq4Xc-3eTjbz-jcQY84V5E1YM71E/view?usp=sharing
📦Collection :
+ prithivMLmods/vision-language-models-67639f790e806e1f9799979f
.
.
.
@prithivMLmods 🤗
- 1 reply
reacted to
singhsidhukuldeep's
post with 🤯
10 days ago
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1604
Excited to share insights from Walmart's groundbreaking semantic search system that revolutionizes e-commerce product discovery!
The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce.
Key Technical Highlights:
• The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching.
• Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types.
• The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs.
• To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples.
Production Implementation Details:
• The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency.
• Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production.
• The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors.
Results:
The system showed significant improvements in both offline metrics and live experiments, with:
- +2.84% improvement in NDCG@10 for human evaluation
- +0.54% lift in Add-to-Cart rates in live A/B testing
This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-
The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce.
Key Technical Highlights:
• The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching.
• Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types.
• The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs.
• To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples.
Production Implementation Details:
• The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency.
• Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production.
• The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors.
Results:
The system showed significant improvements in both offline metrics and live experiments, with:
- +2.84% improvement in NDCG@10 for human evaluation
- +0.54% lift in Add-to-Cart rates in live A/B testing
This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-
- 1 reply
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