Ksenia Se
@kseniase.bsky.social
Writing TuringPost.com, learning and educating about machine learning and AI.
Working on a book about Citizen Diplomacy.
Living in the woods.
Also - being mom to four boys and one baby girl 🤘🏻
Working on a book about Citizen Diplomacy.
Living in the woods.
Also - being mom to four boys and one baby girl 🤘🏻
People want to understand agentic infrastructure protocols better. The strong response to our MCP article shows there’s real demand for clarity around standardization of AI ecosystems
Since so many people asked, we are making our article on Agent2Agent (A2A) free to read on @hf.co
🧵
Since so many people asked, we are making our article on Agent2Agent (A2A) free to read on @hf.co
🧵
🦸🏻#17: What is A2A and why is it – still! – underappreciated?
A Blog post by Ksenia Se on Hugging Face
huggingface.co
May 10, 2025 at 8:45 AM
People want to understand agentic infrastructure protocols better. The strong response to our MCP article shows there’s real demand for clarity around standardization of AI ecosystems
Since so many people asked, we are making our article on Agent2Agent (A2A) free to read on @hf.co
🧵
Since so many people asked, we are making our article on Agent2Agent (A2A) free to read on @hf.co
🧵
The most important features of LFMs (Liquid Foundation Models) from Liquid AI?
Memory-efficiency, inference speed, without compromising model quality.
LFMs have been benchmarked on real hardware, proving that they can beat Transformers.
Liquid AI have also just released Hyena Edge👇
Memory-efficiency, inference speed, without compromising model quality.
LFMs have been benchmarked on real hardware, proving that they can beat Transformers.
Liquid AI have also just released Hyena Edge👇
May 5, 2025 at 8:33 AM
The most important features of LFMs (Liquid Foundation Models) from Liquid AI?
Memory-efficiency, inference speed, without compromising model quality.
LFMs have been benchmarked on real hardware, proving that they can beat Transformers.
Liquid AI have also just released Hyena Edge👇
Memory-efficiency, inference speed, without compromising model quality.
LFMs have been benchmarked on real hardware, proving that they can beat Transformers.
Liquid AI have also just released Hyena Edge👇
What happens when the biggest advocate for coding literacy starts telling people not to learn to code?
In the new Inference episode, I sat down with Amjad Masad, CEO and co-founder at Replit, to discuss the evolution in coding.
Are we entering a post-coding world?
www.youtube.com/watch?v=PlDe...
In the new Inference episode, I sat down with Amjad Masad, CEO and co-founder at Replit, to discuss the evolution in coding.
Are we entering a post-coding world?
www.youtube.com/watch?v=PlDe...
When Will We Stop Coding? A conversation with Amjad Masad, CEO and co-founder @ Replit
YouTube video by Turing Post
www.youtube.com
May 4, 2025 at 11:13 PM
What happens when the biggest advocate for coding literacy starts telling people not to learn to code?
In the new Inference episode, I sat down with Amjad Masad, CEO and co-founder at Replit, to discuss the evolution in coding.
Are we entering a post-coding world?
www.youtube.com/watch?v=PlDe...
In the new Inference episode, I sat down with Amjad Masad, CEO and co-founder at Replit, to discuss the evolution in coding.
Are we entering a post-coding world?
www.youtube.com/watch?v=PlDe...
Top 7 research papers of the week:
▪️ Test-Time Reinforcement Learning
▪️ LLMs are Greedy Agents
▪️ Paper2Code
▪️ Efficient Pretraining Length Scaling
▪️ The Sparse Frontier
▪️ Roll the dice & look before you leap
▪️ Discovering and Analyzing Values in Real-World Language Model Interactions
🧵
▪️ Test-Time Reinforcement Learning
▪️ LLMs are Greedy Agents
▪️ Paper2Code
▪️ Efficient Pretraining Length Scaling
▪️ The Sparse Frontier
▪️ Roll the dice & look before you leap
▪️ Discovering and Analyzing Values in Real-World Language Model Interactions
🧵
April 30, 2025 at 11:44 PM
Top 7 research papers of the week:
▪️ Test-Time Reinforcement Learning
▪️ LLMs are Greedy Agents
▪️ Paper2Code
▪️ Efficient Pretraining Length Scaling
▪️ The Sparse Frontier
▪️ Roll the dice & look before you leap
▪️ Discovering and Analyzing Values in Real-World Language Model Interactions
🧵
▪️ Test-Time Reinforcement Learning
▪️ LLMs are Greedy Agents
▪️ Paper2Code
▪️ Efficient Pretraining Length Scaling
▪️ The Sparse Frontier
▪️ Roll the dice & look before you leap
▪️ Discovering and Analyzing Values in Real-World Language Model Interactions
🧵
9 notable AI models of the week:
▪️ Hyena Edge
▪️ Tina: Tiny Reasoning Models via LoRA
▪️ Kimi-Audio
▪️ Aimo-2 winning solution
▪️ Eagle 2.5
▪️ Trillion-7B
▪️ Surya OCR
▪️ ThinkPRM
▪️ Skywork R1V2
🧵
▪️ Hyena Edge
▪️ Tina: Tiny Reasoning Models via LoRA
▪️ Kimi-Audio
▪️ Aimo-2 winning solution
▪️ Eagle 2.5
▪️ Trillion-7B
▪️ Surya OCR
▪️ ThinkPRM
▪️ Skywork R1V2
🧵
April 29, 2025 at 11:12 AM
9 notable AI models of the week:
▪️ Hyena Edge
▪️ Tina: Tiny Reasoning Models via LoRA
▪️ Kimi-Audio
▪️ Aimo-2 winning solution
▪️ Eagle 2.5
▪️ Trillion-7B
▪️ Surya OCR
▪️ ThinkPRM
▪️ Skywork R1V2
🧵
▪️ Hyena Edge
▪️ Tina: Tiny Reasoning Models via LoRA
▪️ Kimi-Audio
▪️ Aimo-2 winning solution
▪️ Eagle 2.5
▪️ Trillion-7B
▪️ Surya OCR
▪️ ThinkPRM
▪️ Skywork R1V2
🧵
Agent2Agent (A2A) is standardizing how multiple autonomous agents communicate seamlessly across applications.
Yet even with the loud launch and 50 big-name partners, Google's A2A remains underappreciated. Why?
Here are several reasons 👇
www.turingpost.com/p/a2a
Yet even with the loud launch and 50 big-name partners, Google's A2A remains underappreciated. Why?
Here are several reasons 👇
www.turingpost.com/p/a2a
April 26, 2025 at 8:42 PM
Agent2Agent (A2A) is standardizing how multiple autonomous agents communicate seamlessly across applications.
Yet even with the loud launch and 50 big-name partners, Google's A2A remains underappreciated. Why?
Here are several reasons 👇
www.turingpost.com/p/a2a
Yet even with the loud launch and 50 big-name partners, Google's A2A remains underappreciated. Why?
Here are several reasons 👇
www.turingpost.com/p/a2a
ChatGPT shifted how we interact with computers. Real-time conversational AI could do something similar for how we interact with each other
ElevenLabs is working to make such communication possible
We talked to it's co-founder and CEO Mati Staniszewski about what the next few years might look like👇
ElevenLabs is working to make such communication possible
We talked to it's co-founder and CEO Mati Staniszewski about what the next few years might look like👇
When Will We Speak Without Language Barrier? A conversation with Mati Staniszewski, CEO @ ElevenLabs
YouTube video by Turing Post
www.youtube.com
April 14, 2025 at 5:25 PM
ChatGPT shifted how we interact with computers. Real-time conversational AI could do something similar for how we interact with each other
ElevenLabs is working to make such communication possible
We talked to it's co-founder and CEO Mati Staniszewski about what the next few years might look like👇
ElevenLabs is working to make such communication possible
We talked to it's co-founder and CEO Mati Staniszewski about what the next few years might look like👇
2 perspectives on Human-AI co-agency
▪️ AI as a medium
▪️ AI as a feedback loop
Details🧵
▪️ AI as a medium
▪️ AI as a feedback loop
Details🧵
🦸🏻#16: Co-Agency as The Ultimate Extension of Human
how AI as a medium shapes our perception, behavior, and culture
www.turingpost.com
April 11, 2025 at 12:18 PM
2 perspectives on Human-AI co-agency
▪️ AI as a medium
▪️ AI as a feedback loop
Details🧵
▪️ AI as a medium
▪️ AI as a feedback loop
Details🧵
The freshest AI/ML research of the week
My top 10:
▪️ AI for Software Engineering
▪️ Inference-Time Scaling for Reward Modeling
▪️ Inference-Time Scaling for Complex Tasks
▪️ Open-Reasoner-Zero
▪️ SynWorld
▪️ Agent S2
▪️ KnowSelf
▪️ ZClip
▪️ MegaScale-Infer
▪️ Scaling Laws in Scientific Discovery
🧵
My top 10:
▪️ AI for Software Engineering
▪️ Inference-Time Scaling for Reward Modeling
▪️ Inference-Time Scaling for Complex Tasks
▪️ Open-Reasoner-Zero
▪️ SynWorld
▪️ Agent S2
▪️ KnowSelf
▪️ ZClip
▪️ MegaScale-Infer
▪️ Scaling Laws in Scientific Discovery
🧵
April 8, 2025 at 12:13 PM
The freshest AI/ML research of the week
My top 10:
▪️ AI for Software Engineering
▪️ Inference-Time Scaling for Reward Modeling
▪️ Inference-Time Scaling for Complex Tasks
▪️ Open-Reasoner-Zero
▪️ SynWorld
▪️ Agent S2
▪️ KnowSelf
▪️ ZClip
▪️ MegaScale-Infer
▪️ Scaling Laws in Scientific Discovery
🧵
My top 10:
▪️ AI for Software Engineering
▪️ Inference-Time Scaling for Reward Modeling
▪️ Inference-Time Scaling for Complex Tasks
▪️ Open-Reasoner-Zero
▪️ SynWorld
▪️ Agent S2
▪️ KnowSelf
▪️ ZClip
▪️ MegaScale-Infer
▪️ Scaling Laws in Scientific Discovery
🧵
7 notable AI models of the week:
• Command A
• TransMamba
• HallOumi
• ScholarCopilot
• OThink-MR1
• RIG
• Z1
🧵
• Command A
• TransMamba
• HallOumi
• ScholarCopilot
• OThink-MR1
• RIG
• Z1
🧵
April 7, 2025 at 10:40 PM
7 notable AI models of the week:
• Command A
• TransMamba
• HallOumi
• ScholarCopilot
• OThink-MR1
• RIG
• Z1
🧵
• Command A
• TransMamba
• HallOumi
• ScholarCopilot
• OThink-MR1
• RIG
• Z1
🧵
9 types of AI inference
5 types based on how the model reasons:
▪️ Probabilistic
▪️ Rule-based
▪️ Logical
▪️ Abductive
▪️ Fuzzy
4 inference types based on its execution contexts:
▪️ Batch
▪️ Real-time
▪️ Edge
▪️ Cloud
Save the list and check this out for useful resources: huggingface.co/posts/Ksenia...
5 types based on how the model reasons:
▪️ Probabilistic
▪️ Rule-based
▪️ Logical
▪️ Abductive
▪️ Fuzzy
4 inference types based on its execution contexts:
▪️ Batch
▪️ Real-time
▪️ Edge
▪️ Cloud
Save the list and check this out for useful resources: huggingface.co/posts/Ksenia...
April 6, 2025 at 8:23 PM
9 types of AI inference
5 types based on how the model reasons:
▪️ Probabilistic
▪️ Rule-based
▪️ Logical
▪️ Abductive
▪️ Fuzzy
4 inference types based on its execution contexts:
▪️ Batch
▪️ Real-time
▪️ Edge
▪️ Cloud
Save the list and check this out for useful resources: huggingface.co/posts/Ksenia...
5 types based on how the model reasons:
▪️ Probabilistic
▪️ Rule-based
▪️ Logical
▪️ Abductive
▪️ Fuzzy
4 inference types based on its execution contexts:
▪️ Batch
▪️ Real-time
▪️ Edge
▪️ Cloud
Save the list and check this out for useful resources: huggingface.co/posts/Ksenia...
A free short AGI safety course from @GoogleDeepMind
It covers important topics, such as:
- Path to advanced AI capabilities
- Sources of misalignment risk
- Technical approaches: safe design patterns, oversight, robust training, etc.
- AI governance
- Testing and evaluating AI failures
Watch 👇
It covers important topics, such as:
- Path to advanced AI capabilities
- Sources of misalignment risk
- Technical approaches: safe design patterns, oversight, robust training, etc.
- AI governance
- Testing and evaluating AI failures
Watch 👇
Google DeepMind AGI Safety Course - YouTube
A short course from Google DeepMind on AGI safety, covering alignment problems we can expect as AI capabilities advance, and our current approach to these pr...
www.youtube.com
April 6, 2025 at 12:16 AM
A free short AGI safety course from @GoogleDeepMind
It covers important topics, such as:
- Path to advanced AI capabilities
- Sources of misalignment risk
- Technical approaches: safe design patterns, oversight, robust training, etc.
- AI governance
- Testing and evaluating AI failures
Watch 👇
It covers important topics, such as:
- Path to advanced AI capabilities
- Sources of misalignment risk
- Technical approaches: safe design patterns, oversight, robust training, etc.
- AI governance
- Testing and evaluating AI failures
Watch 👇
Key concepts of AI inference:
- Inference time
- Total generation time
- Latency
- Time to First Token (TTFT)
- Time Per Output Token (TPOT)
- Throughput
- Cost per inference
- Scalability
- Accuracy
- Entropy
Learn what they mean and other main things about inference in our article:
- Inference time
- Total generation time
- Latency
- Time to First Token (TTFT)
- Time Per Output Token (TPOT)
- Throughput
- Cost per inference
- Scalability
- Accuracy
- Entropy
Learn what they mean and other main things about inference in our article:
Topic 34: Things You Need to Know About Inference
we dive into the core aspects of AI inference, explore how it works and what can make it faster
www.turingpost.com
April 3, 2025 at 12:47 AM
Key concepts of AI inference:
- Inference time
- Total generation time
- Latency
- Time to First Token (TTFT)
- Time Per Output Token (TPOT)
- Throughput
- Cost per inference
- Scalability
- Accuracy
- Entropy
Learn what they mean and other main things about inference in our article:
- Inference time
- Total generation time
- Latency
- Time to First Token (TTFT)
- Time Per Output Token (TPOT)
- Throughput
- Cost per inference
- Scalability
- Accuracy
- Entropy
Learn what they mean and other main things about inference in our article:
3 studies from @anthropicai.bsky.social that form a "guide" to the LM's mind:
▪️"Dictionary learning"
▪️ Monosemanticity
▪️ "On the Biology of a Large Language Model"
🧵
▪️"Dictionary learning"
▪️ Monosemanticity
▪️ "On the Biology of a Large Language Model"
🧵
April 2, 2025 at 1:48 PM
3 studies from @anthropicai.bsky.social that form a "guide" to the LM's mind:
▪️"Dictionary learning"
▪️ Monosemanticity
▪️ "On the Biology of a Large Language Model"
🧵
▪️"Dictionary learning"
▪️ Monosemanticity
▪️ "On the Biology of a Large Language Model"
🧵
4 notable AI models of the week:
• Google's Gemini 2.5
• DeepSeek-V3-0324
• Zhipu's AutoGLM Rumination
• Alibaba's Qwen2.5-Omni
🧵
• Google's Gemini 2.5
• DeepSeek-V3-0324
• Zhipu's AutoGLM Rumination
• Alibaba's Qwen2.5-Omni
🧵
April 1, 2025 at 11:02 AM
4 notable AI models of the week:
• Google's Gemini 2.5
• DeepSeek-V3-0324
• Zhipu's AutoGLM Rumination
• Alibaba's Qwen2.5-Omni
🧵
• Google's Gemini 2.5
• DeepSeek-V3-0324
• Zhipu's AutoGLM Rumination
• Alibaba's Qwen2.5-Omni
🧵
9 Multimodal Chain-of-Thought methods
▪️ KAM-CoT
▪️ Multimodal Visualization-of-Thought (MVoT)
▪️ Compositional CoT
▪️ URSA
▪️ MM-Verify
▪️ Duty-Distinct CoT
▪️ Multimodal-CoT
▪️ Graph-of-Thought
▪️ Hypergraph-of-Thought
Save the list, and check this out for more info: huggingface.co/posts/Ksenia...
▪️ KAM-CoT
▪️ Multimodal Visualization-of-Thought (MVoT)
▪️ Compositional CoT
▪️ URSA
▪️ MM-Verify
▪️ Duty-Distinct CoT
▪️ Multimodal-CoT
▪️ Graph-of-Thought
▪️ Hypergraph-of-Thought
Save the list, and check this out for more info: huggingface.co/posts/Ksenia...
March 30, 2025 at 1:07 PM
9 Multimodal Chain-of-Thought methods
▪️ KAM-CoT
▪️ Multimodal Visualization-of-Thought (MVoT)
▪️ Compositional CoT
▪️ URSA
▪️ MM-Verify
▪️ Duty-Distinct CoT
▪️ Multimodal-CoT
▪️ Graph-of-Thought
▪️ Hypergraph-of-Thought
Save the list, and check this out for more info: huggingface.co/posts/Ksenia...
▪️ KAM-CoT
▪️ Multimodal Visualization-of-Thought (MVoT)
▪️ Compositional CoT
▪️ URSA
▪️ MM-Verify
▪️ Duty-Distinct CoT
▪️ Multimodal-CoT
▪️ Graph-of-Thought
▪️ Hypergraph-of-Thought
Save the list, and check this out for more info: huggingface.co/posts/Ksenia...
Humans as tools? Is it real?
Yes, sometimes human is just another callable function in an AI agent's toolbox.
▪️ Human in the loop (HITL) is a design pattern, where humans are built into the decision loop to:
- validate outputs
- steer actions
- override the machine when necessary
🧵
Yes, sometimes human is just another callable function in an AI agent's toolbox.
▪️ Human in the loop (HITL) is a design pattern, where humans are built into the decision loop to:
- validate outputs
- steer actions
- override the machine when necessary
🧵
March 30, 2025 at 10:10 AM
Humans as tools? Is it real?
Yes, sometimes human is just another callable function in an AI agent's toolbox.
▪️ Human in the loop (HITL) is a design pattern, where humans are built into the decision loop to:
- validate outputs
- steer actions
- override the machine when necessary
🧵
Yes, sometimes human is just another callable function in an AI agent's toolbox.
▪️ Human in the loop (HITL) is a design pattern, where humans are built into the decision loop to:
- validate outputs
- steer actions
- override the machine when necessary
🧵
What is Slim Attention?
It's a new attention mechanism that allows models to be 2x faster and cut memory use by 32 times!
▪️ What's the secret?
It uses the same math as Multi-Head Attention (MHA) but applies one clever trick🧵
It's a new attention mechanism that allows models to be 2x faster and cut memory use by 32 times!
▪️ What's the secret?
It uses the same math as Multi-Head Attention (MHA) but applies one clever trick🧵
March 29, 2025 at 11:15 PM
What is Slim Attention?
It's a new attention mechanism that allows models to be 2x faster and cut memory use by 32 times!
▪️ What's the secret?
It uses the same math as Multi-Head Attention (MHA) but applies one clever trick🧵
It's a new attention mechanism that allows models to be 2x faster and cut memory use by 32 times!
▪️ What's the secret?
It uses the same math as Multi-Head Attention (MHA) but applies one clever trick🧵
Because of DeepSeek, Qwen models are a little overlooked but they are worth your attention. It's very interesting how new Qwen2.5-Omni truly expands multimodality to a full pack — text, images, audio, and video. Its main focus is speech generation, and it uses a treasure trove of power features 👇
March 29, 2025 at 11:05 AM
Because of DeepSeek, Qwen models are a little overlooked but they are worth your attention. It's very interesting how new Qwen2.5-Omni truly expands multimodality to a full pack — text, images, audio, and video. Its main focus is speech generation, and it uses a treasure trove of power features 👇
Do LLMs "know" more facts deep inside their systems than they actually "say"?
Researchers from Technion and Google Research defined what this "knowing" means and also explored it and found that:
LLMs often know more than they say but getting them to "say" it can be surprisingly hard
Key findings🧵
Researchers from Technion and Google Research defined what this "knowing" means and also explored it and found that:
LLMs often know more than they say but getting them to "say" it can be surprisingly hard
Key findings🧵
March 27, 2025 at 12:02 AM
Do LLMs "know" more facts deep inside their systems than they actually "say"?
Researchers from Technion and Google Research defined what this "knowing" means and also explored it and found that:
LLMs often know more than they say but getting them to "say" it can be surprisingly hard
Key findings🧵
Researchers from Technion and Google Research defined what this "knowing" means and also explored it and found that:
LLMs often know more than they say but getting them to "say" it can be surprisingly hard
Key findings🧵
The freshest AI/ML research of the week:
Our top 2
▪️ Xattention
▪️ Inside-Out: Hidden Factual Knowledge in LLMs
▪️ Rwkv-7 "Goose"
▪️ ϕ-Decoding
▪️ Frac-connections
▪️ DAPO
▪️ Reinforcement learning for reasoning in small LLMs
▪️ MetaLadder
▪️ Why do multi-agent LLM systems fail?
🧵
Our top 2
▪️ Xattention
▪️ Inside-Out: Hidden Factual Knowledge in LLMs
▪️ Rwkv-7 "Goose"
▪️ ϕ-Decoding
▪️ Frac-connections
▪️ DAPO
▪️ Reinforcement learning for reasoning in small LLMs
▪️ MetaLadder
▪️ Why do multi-agent LLM systems fail?
🧵
March 25, 2025 at 3:57 PM
The freshest AI/ML research of the week:
Our top 2
▪️ Xattention
▪️ Inside-Out: Hidden Factual Knowledge in LLMs
▪️ Rwkv-7 "Goose"
▪️ ϕ-Decoding
▪️ Frac-connections
▪️ DAPO
▪️ Reinforcement learning for reasoning in small LLMs
▪️ MetaLadder
▪️ Why do multi-agent LLM systems fail?
🧵
Our top 2
▪️ Xattention
▪️ Inside-Out: Hidden Factual Knowledge in LLMs
▪️ Rwkv-7 "Goose"
▪️ ϕ-Decoding
▪️ Frac-connections
▪️ DAPO
▪️ Reinforcement learning for reasoning in small LLMs
▪️ MetaLadder
▪️ Why do multi-agent LLM systems fail?
🧵
7 open-source AI models of the week:
• KBLaM from @microsoft.com
• Fin-R1
• NVIDIA’s Cosmos-Reason1
• NVIDIA’s Cosmos-Transfer1
• M3 by NVIDIA
• Tencent’s T1
• Roblox’ Cube
🧵
• KBLaM from @microsoft.com
• Fin-R1
• NVIDIA’s Cosmos-Reason1
• NVIDIA’s Cosmos-Transfer1
• M3 by NVIDIA
• Tencent’s T1
• Roblox’ Cube
🧵
March 24, 2025 at 11:57 PM
7 open-source AI models of the week:
• KBLaM from @microsoft.com
• Fin-R1
• NVIDIA’s Cosmos-Reason1
• NVIDIA’s Cosmos-Transfer1
• M3 by NVIDIA
• Tencent’s T1
• Roblox’ Cube
🧵
• KBLaM from @microsoft.com
• Fin-R1
• NVIDIA’s Cosmos-Reason1
• NVIDIA’s Cosmos-Transfer1
• M3 by NVIDIA
• Tencent’s T1
• Roblox’ Cube
🧵
The interview with SharonZhou, Co-founder & CEO of
Lamini, and co-creator of a top AI course on Coursera, was full of incredible insights!
We discussed important topics, such as:
- AI hallucinations
- Agents and RAG hype
- The keys to GenAI
- AI education
www.youtube.com/watch?v=RW01...
Lamini, and co-creator of a top AI course on Coursera, was full of incredible insights!
We discussed important topics, such as:
- AI hallucinations
- Agents and RAG hype
- The keys to GenAI
- AI education
www.youtube.com/watch?v=RW01...
Turing Post / Inference. Ep 001: Sharon Zhou, Lamini (and so much more)
YouTube video by Turing Post
www.youtube.com
March 24, 2025 at 11:37 PM
The interview with SharonZhou, Co-founder & CEO of
Lamini, and co-creator of a top AI course on Coursera, was full of incredible insights!
We discussed important topics, such as:
- AI hallucinations
- Agents and RAG hype
- The keys to GenAI
- AI education
www.youtube.com/watch?v=RW01...
Lamini, and co-creator of a top AI course on Coursera, was full of incredible insights!
We discussed important topics, such as:
- AI hallucinations
- Agents and RAG hype
- The keys to GenAI
- AI education
www.youtube.com/watch?v=RW01...
8 types of RoPE (Rotary Position Embedding):
▪️ Original RoPE
▪️ LongRoPE
▪️ LongRoPE2
▪️ MRoPE (Multimodal RoPE)
▪️ DRoPE (Directional RoPE)
▪️ VideoRoPE
▪️ VRoPE
▪️ XPos
Save the list and check this out for the links and more info: huggingface.co/posts/Ksenia...
▪️ Original RoPE
▪️ LongRoPE
▪️ LongRoPE2
▪️ MRoPE (Multimodal RoPE)
▪️ DRoPE (Directional RoPE)
▪️ VideoRoPE
▪️ VRoPE
▪️ XPos
Save the list and check this out for the links and more info: huggingface.co/posts/Ksenia...
March 24, 2025 at 12:20 AM
8 types of RoPE (Rotary Position Embedding):
▪️ Original RoPE
▪️ LongRoPE
▪️ LongRoPE2
▪️ MRoPE (Multimodal RoPE)
▪️ DRoPE (Directional RoPE)
▪️ VideoRoPE
▪️ VRoPE
▪️ XPos
Save the list and check this out for the links and more info: huggingface.co/posts/Ksenia...
▪️ Original RoPE
▪️ LongRoPE
▪️ LongRoPE2
▪️ MRoPE (Multimodal RoPE)
▪️ DRoPE (Directional RoPE)
▪️ VideoRoPE
▪️ VRoPE
▪️ XPos
Save the list and check this out for the links and more info: huggingface.co/posts/Ksenia...
What is Qwen-Agent framework?
It's a full‑fledged agentic ecosystem that lets Qwen models autonomously plan, call functions, and execute complex, multi‑step tasks right out of the box.
Here are examples of Qwen-Agent's applications:
It's a full‑fledged agentic ecosystem that lets Qwen models autonomously plan, call functions, and execute complex, multi‑step tasks right out of the box.
Here are examples of Qwen-Agent's applications:
March 22, 2025 at 11:32 PM
What is Qwen-Agent framework?
It's a full‑fledged agentic ecosystem that lets Qwen models autonomously plan, call functions, and execute complex, multi‑step tasks right out of the box.
Here are examples of Qwen-Agent's applications:
It's a full‑fledged agentic ecosystem that lets Qwen models autonomously plan, call functions, and execute complex, multi‑step tasks right out of the box.
Here are examples of Qwen-Agent's applications: