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
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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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AdinaY's activity
reacted to
nataliaElv's
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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
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
989
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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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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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
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3 days ago
Article
MiniMax-01 is Now Open-Source: Scaling Lightning Attention for the AI Agent Era
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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
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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