A Short Summary of Chinese AI Global Expansion
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Adina Yakefu
AdinaY
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New chapter in the Hugging Face NLP course! 🤗 🚀
We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub.
Any feedback for improvements welcome!
https://huggingface.co/learn/nlp-course/chapter10
posted
an
update
1 day ago
New work from Alibaba_Qwen🔥
Qwen2.5-Math-PRM 7B & 72B 🔢 Process Reward Models for enhanced process supervision in the mathematical reasoning of LLMs.
Paper:
https://huggingface.co/papers/2501.07301
Model:
https://huggingface.co/Qwen/Qwen2.5-Math-PRM-7B
https://huggingface.co/Qwen/Qwen2.5-Math-PRM-72B
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🔢 Math models
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AdinaY's activity
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nataliaElv's
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about 17 hours ago
Post
731
New chapter in the Hugging Face NLP course! 🤗 🚀
We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub.
Any feedback for improvements welcome!
https://huggingface.co/learn/nlp-course/chapter10
We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub.
Any feedback for improvements welcome!
https://huggingface.co/learn/nlp-course/chapter10
posted
an
update
1 day ago
Post
959
New work from Alibaba_Qwen🔥
Qwen2.5-Math-PRM 7B & 72B 🔢 Process Reward Models for enhanced process supervision in the mathematical reasoning of LLMs.
Paper:
The Lessons of Developing Process Reward Models in Mathematical Reasoning (2501.07301)
Model:
Qwen/Qwen2.5-Math-PRM-7B
Qwen/Qwen2.5-Math-PRM-72B
Qwen2.5-Math-PRM 7B & 72B 🔢 Process Reward Models for enhanced process supervision in the mathematical reasoning of LLMs.
Paper:
The Lessons of Developing Process Reward Models in Mathematical Reasoning (2501.07301)
Model:
Qwen/Qwen2.5-Math-PRM-7B
Qwen/Qwen2.5-Math-PRM-72B
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burtenshaw's
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2 days ago
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22569
We’re launching a FREE and CERTIFIED course on Agents!
We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.
Here's what you'll learn:
- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions.
- Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors.
- Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents.
- Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents.
Audience
This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.
Enroll today and start building the next generation of AI agent applications!
https://bit.ly/hf-learn-agents
We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.
Here's what you'll learn:
- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions.
- Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors.
- Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents.
- Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents.
Audience
This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.
Enroll today and start building the next generation of AI agent applications!
https://bit.ly/hf-learn-agents
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m-ric's
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2 days ago
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2145
𝗪𝗲'𝘃𝗲 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 𝘃𝟭.𝟯.𝟬 🚀, and it comes with a major feature: you can now log agent runs using OpenTelemetry to inspect them afterwards! 📊
This interactive format is IMO much easier to inspect big multi-step runs than endless console logs.
The setup is very easy, in a few lines of code.
Find a tutorial here 👉 https://huggingface.co/docs/smolagents/tutorials/inspect_runs
This interactive format is IMO much easier to inspect big multi-step runs than endless console logs.
The setup is very easy, in a few lines of code.
Find a tutorial here 👉 https://huggingface.co/docs/smolagents/tutorials/inspect_runs
- 4 replies
posted
an
update
3 days ago
Post
2130
InternLM3-8B-instruct🔥 Trained on just 4T tokens, it outperforms Llama3.1-8B and Qwen2.5-7B in reasoning tasks, at 75% lower cost!
internlm/internlm3-67875827c377690c01a9131d
internlm/internlm3-67875827c377690c01a9131d
posted
an
update
4 days ago
Post
2888
MiniMax, the company behind Hailuo_AI, has joined the open source community by releasing both models and demos of MiniMax-Text-01 & MiniMax-VL-01🔥
- Model
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
- Demo
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
✨ MiniMax-text-01:
- 456B with 45.9B activated per token
- Combines Lightning Attention, Softmax Attention, and MoE for optimal performance
- Training context up to 1M tokens, inference handles 4M tokens
✨ MiniMax-VL-01:
- ViT-MLP-LLM framework ( non-transformer👀)
- Handles image inputs from 336×336 to 2016×2016
- 694M image-caption pairs + 512B tokens processed across 4 stages
- Model
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
- Demo
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
✨ MiniMax-text-01:
- 456B with 45.9B activated per token
- Combines Lightning Attention, Softmax Attention, and MoE for optimal performance
- Training context up to 1M tokens, inference handles 4M tokens
✨ MiniMax-VL-01:
- ViT-MLP-LLM framework ( non-transformer👀)
- Handles image inputs from 336×336 to 2016×2016
- 694M image-caption pairs + 512B tokens processed across 4 stages
- 1 reply
posted
an
update
4 days ago
Post
3094
MiniCPM-o2.6 🔥 an end-side multimodal LLMs released by OpenBMB from the Chinese community
Model: openbmb/MiniCPM-o-2_6
✨ Real-time English/Chinese conversation, emotion control and ASR/STT
✨ Real-time video/audio understanding
✨ Processes up to 1.8M pixels, leads OCRBench & supports 30+ languages
Model: openbmb/MiniCPM-o-2_6
✨ Real-time English/Chinese conversation, emotion control and ASR/STT
✨ Real-time video/audio understanding
✨ Processes up to 1.8M pixels, leads OCRBench & supports 30+ languages
posted
an
update
8 days ago
Post
587
LLaVA-Mini🔥 A efficient multimodal model for image and video understanding released by Chinese Academy of Sciences
Model: ICTNLP/llava-mini-llama-3.1-8b
Paper: LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token (2501.03895)
✨ Matches LLaVA-v1.5 using just 1 vision token
✨ Delivers <40ms response time
✨ Reduces vision tokens while maintaining strong visual understanding
Model: ICTNLP/llava-mini-llama-3.1-8b
Paper: LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token (2501.03895)
✨ Matches LLaVA-v1.5 using just 1 vision token
✨ Delivers <40ms response time
✨ Reduces vision tokens while maintaining strong visual understanding
posted
an
update
12 days ago
Post
2328
Excited to see Alibaba DAMO Academy release a multimodel dataset for vision language pretraining on the hub🔥
Paper: 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining (2501.00958)
Dataset: DAMO-NLP-SG/multimodal_textbook
✨ 6.5M images + 0.8B text from 22k hours of instructional videos
✨ Covers subjects like math, physics, and chemistry
✨ Apache 2.0
Paper: 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining (2501.00958)
Dataset: DAMO-NLP-SG/multimodal_textbook
✨ 6.5M images + 0.8B text from 22k hours of instructional videos
✨ Covers subjects like math, physics, and chemistry
✨ Apache 2.0
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3285
🎄 Here Before - Xmas🎅✨
🧑🏻🎄Models
+ [ Xmas 2D Illustration ] : strangerzonehf/Flux-Xmas-Illustration-LoRA
+ [ Xmas 3D Art ] : strangerzonehf/Flux-Xmas-3D-LoRA
+ [ Xmas Chocolate ] : strangerzonehf/Flux-Xmas-Chocolate-LoRA
+ [ Xmas Isometric Kit ] : strangerzonehf/Flux-Xmas-Isometric-Kit-LoRA
+ [ Xmas Realpix ] : strangerzonehf/Flux-Xmas-Realpix-LoRA
+ [ Xmas Anime ] : strangerzonehf/Flux-Anime-Xmas-LoRA
❄️Collections
+ [ Xmas Art ] : strangerzonehf/christmas-pack-6758b199487adafaddb68f82
+ [ Stranger Zone Collection ] : prithivMLmods/stranger-zone-collections-org-6737118adcf2cb40d66d0c7e
🥶Page
+ [ Stranger Zone ] : https://huggingface.co/strangerzonehf
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@prithivMLmods 🤗
🧑🏻🎄Models
+ [ Xmas 2D Illustration ] : strangerzonehf/Flux-Xmas-Illustration-LoRA
+ [ Xmas 3D Art ] : strangerzonehf/Flux-Xmas-3D-LoRA
+ [ Xmas Chocolate ] : strangerzonehf/Flux-Xmas-Chocolate-LoRA
+ [ Xmas Isometric Kit ] : strangerzonehf/Flux-Xmas-Isometric-Kit-LoRA
+ [ Xmas Realpix ] : strangerzonehf/Flux-Xmas-Realpix-LoRA
+ [ Xmas Anime ] : strangerzonehf/Flux-Anime-Xmas-LoRA
❄️Collections
+ [ Xmas Art ] : strangerzonehf/christmas-pack-6758b199487adafaddb68f82
+ [ Stranger Zone Collection ] : prithivMLmods/stranger-zone-collections-org-6737118adcf2cb40d66d0c7e
🥶Page
+ [ Stranger Zone ] : https://huggingface.co/strangerzonehf
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@prithivMLmods 🤗
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prithivMLmods's
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12 days ago
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2536
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
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@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
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@prithivMLmods 🤗
- 1 reply
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prithivMLmods's
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3848
Triangulum Catalogued 🔥💫
🎯Triangulum is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.
+ Triangulum-10B : prithivMLmods/Triangulum-10B
+ Quants : prithivMLmods/Triangulum-10B-GGUF
+ Triangulum-5B : prithivMLmods/Triangulum-5B
+ Quants : prithivMLmods/Triangulum-5B-GGUF
+ Triangulum-1B : prithivMLmods/Triangulum-1B
+ Quants : prithivMLmods/Triangulum-1B-GGUF
🎯Triangulum is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.
+ Triangulum-10B : prithivMLmods/Triangulum-10B
+ Quants : prithivMLmods/Triangulum-10B-GGUF
+ Triangulum-5B : prithivMLmods/Triangulum-5B
+ Quants : prithivMLmods/Triangulum-5B-GGUF
+ Triangulum-1B : prithivMLmods/Triangulum-1B
+ Quants : prithivMLmods/Triangulum-1B-GGUF
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reacted to
roseking's
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12 days ago
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2631
🤗 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
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- 3 replies