Loading video...

Video Failed to Load

Go Home

Model merging is tricky when model weights aren’t aligned Introducing KnOTS 🪢: a gradient-free framework to merge LoRA models. KnOTS is plug-and-play, boosting SoTA merging methods by up to 4.3%🚀 📜: 💻:

LChoshen's profile picture

Leshem (Legend) Choshen 🤖🤗 @NeurIPS

5,100 subscribers

26,186 views • 1 year ago •via X (Twitter)

EducationNews & PoliticsScience & Technology
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Introducing screengrasp - The new SOTA agent click position model - beating Anthropic Computer Use, OmniParser and Molmo by a wide margin. - Simplified API access to the best click models on the market. 🔁Retweet and DM for free API credits. More details 👇
0:18

Introducing screengrasp - The new SOTA agent click position model - beating Anthropic Computer Use, OmniParser and Molmo by a wide margin. - Simplified API access to the best click models on the market. 🔁Retweet and DM for free API credits. More details 👇

Florian S

54,319 views • 1 year ago

When you realize "knots" literally meant knots in a rope
0:13

When you realize "knots" literally meant knots in a rope

Interesting As Fuck

93,398 views • 10 months ago

1/ Introducing Glider - the smallest model to beat GPT-4o-mini on eval tasks ⚡🚀 - Open source, open weights, open code - Explainable evaluations by nature - Trained on 183 criteria and 685 domains Try it out for free at 🔥
0:20

1/ Introducing Glider - the smallest model to beat GPT-4o-mini on eval tasks ⚡🚀 - Open source, open weights, open code - Explainable evaluations by nature - Trained on 183 criteria and 685 domains Try it out for free at 🔥

PatronusAI

14,842 views • 1 year ago

A site dedicated to animated knots. 100s of knots. All animated. All useful.
0:10

A site dedicated to animated knots. 100s of knots. All animated. All useful.

Abakcus

15,444 views • 11 months ago

Reasoning models often waste tokens self-doubting. Dynasor saves you up to 81% tokens to arrive at the correct answer! 🧠✂️ - Probe the model halfway to get the certainty - Use Certainty to stop reasoning - 100% Training-Free, Plug-and-play 🎮Demo:
0:17

Reasoning models often waste tokens self-doubting. Dynasor saves you up to 81% tokens to arrive at the correct answer! 🧠✂️ - Probe the model halfway to get the certainty - Use Certainty to stop reasoning - 100% Training-Free, Plug-and-play 🎮Demo:

Hao AI Lab

99,536 views • 1 year ago

Introducing ✨ Aya Vision ✨ - an open-weights model to connect our world through language and vision Aya Vision adds breakthrough multimodal capabilities to our state-of-the-art multilingual 8B and 32B models. 🌿
0:13

Introducing ✨ Aya Vision ✨ - an open-weights model to connect our world through language and vision Aya Vision adds breakthrough multimodal capabilities to our state-of-the-art multilingual 8B and 32B models. 🌿

Cohere Labs

206,502 views • 1 year ago

This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.
0:10

This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.

Physical Intelligence

120,595 views • 6 months ago

Protein–ligand cofolding models are getting incredibly powerful… but do they have to be so slow? 🧬🐢💊 Our new preprint introduces a new flow-map framework called DeCAF for fast few-step cofolding — up to 5× faster while preserving sample quality on the SOTA Pearl model and 20x faster than Boltz 1x. ⚡🧵 📜 Blog: 🔗arXiv: Code (coming soon):
0:11

Protein–ligand cofolding models are getting incredibly powerful… but do they have to be so slow? 🧬🐢💊 Our new preprint introduces a new flow-map framework called DeCAF for fast few-step cofolding — up to 5× faster while preserving sample quality on the SOTA Pearl model and 20x faster than Boltz 1x. ⚡🧵 📜 Blog: 🔗arXiv: Code (coming soon):

Joey Bose

19,785 views • 7 days ago

Introducing OrdiBird. OrdiBird is a bitcoin inscription navigating node mem pools to get to the miner mem pool. Flap to avoid garbagemen and knots. Win and earn a BIRD token on Spark. Link to the game in the post below.
0:19

Introducing OrdiBird. OrdiBird is a bitcoin inscription navigating node mem pools to get to the miner mem pool. Flap to avoid garbagemen and knots. Win and earn a BIRD token on Spark. Link to the game in the post below.

ecurrencyhodler

12,036 views • 9 months ago

Our latest language model, Hermes 4, is FREE through the entire holiday weekend! Try an AI that's aligned to you.
0:14

Our latest language model, Hermes 4, is FREE through the entire holiday weekend! Try an AI that's aligned to you.

Nous Research

441,225 views • 9 months ago

Introducing DeepThought-8B: Transparent reasoning model built on LLaMA-3.1 with test-time compute scaling. - JSON-structured thought chains & controllable inference paths. - ~16GB VRAM, competitive w/ 70B models. - Open model weights, and inference scripts.
0:16

Introducing DeepThought-8B: Transparent reasoning model built on LLaMA-3.1 with test-time compute scaling. - JSON-structured thought chains & controllable inference paths. - ~16GB VRAM, competitive w/ 70B models. - Open model weights, and inference scripts.

Ruliad

219,315 views • 1 year ago

useful knots when I can remember how to do them.
0:18

useful knots when I can remember how to do them.

Tawadotcom

254,882 views • 11 months ago

Bro doesn’t know what a zipper merge is He absolutely laid on his horn Everyone else in traffic was merging every-other, except this guy 😂
0:18

Bro doesn’t know what a zipper merge is He absolutely laid on his horn Everyone else in traffic was merging every-other, except this guy 😂

The Cybertruck Guy

20,949 views • 1 year ago

Vibe coding but you’ve got to be careful when merging everything to production. 🫠 Now managing 300K+ users and ~60K MAU, I have to double-check every production merge. A single crash can cost $500–$700 a day… and a flood of 1⭐ reviews.
0:13

Vibe coding but you’ve got to be careful when merging everything to production. 🫠 Now managing 300K+ users and ~60K MAU, I have to double-check every production merge. A single crash can cost $500–$700 a day… and a flood of 1⭐ reviews.

Alex Nguyen

78,707 views • 8 months ago

Introducing 𝗦𝘂𝗽𝗲𝗿 𝗝𝗦𝗢𝗡 𝗠𝗼𝗱𝗲, a framework for low latency structured output generation from LLMs. Generate JSON up to 𝟮𝟬𝘅 𝗳𝗮𝘀𝘁𝗲𝗿 from OpenAI and open source models. ❌ No need to threaten the model, tip the AI, etc ❌ Built with Alex Derhacobian 🔧 🧵👇
0:14

Introducing 𝗦𝘂𝗽𝗲𝗿 𝗝𝗦𝗢𝗡 𝗠𝗼𝗱𝗲, a framework for low latency structured output generation from LLMs. Generate JSON up to 𝟮𝟬𝘅 𝗳𝗮𝘀𝘁𝗲𝗿 from OpenAI and open source models. ❌ No need to threaten the model, tip the AI, etc ❌ Built with Alex Derhacobian 🔧 🧵👇

Varun Shenoy

166,119 views • 2 years ago

Most image stacks bet on one giant, closed model. We don’t. We route each image gen request on Syntax to the best open-source model/LoRA for the job. Quality rises, iteration speeds up, costs stay sane, and creators aren’t locked into one model’s quirks. The future is orchestration, not monoliths.
0:10

Most image stacks bet on one giant, closed model. We don’t. We route each image gen request on Syntax to the best open-source model/LoRA for the job. Quality rises, iteration speeds up, costs stay sane, and creators aren’t locked into one model’s quirks. The future is orchestration, not monoliths.

Spectral

13,239 views • 8 months ago

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.
0:17

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

AK

375,080 views • 3 years ago

9️⃣3️⃣ KNOTS IT UP!
0:15

9️⃣3️⃣ KNOTS IT UP!

Boston Bruins

16,555 views • 6 months ago

Merge is Live The current version is desktop-only to better test UX and on-chain functionality. Mobile support will be added soon. All other features are already working. Use yesterday’s article as a guide for merging your tokens. Rarity and supply are now under community control.
0:13

Merge is Live The current version is desktop-only to better test UX and on-chain functionality. Mobile support will be added soon. All other features are already working. Use yesterday’s article as a guide for merging your tokens. Rarity and supply are now under community control.

Rilato

25,259 views • 1 month ago

The King is coming back. Free to play for everyone. Learn the model. Probe the edges. Level up. Test Stormrae and get ready for the next releases. Free prompts, more shards.
0:18

The King is coming back. Free to play for everyone. Learn the model. Probe the edges. Level up. Test Stormrae and get ready for the next releases. Free prompts, more shards.

Stormrae

37,497 views • 2 months ago