VDR Official X
February 10, 2026
bycloud • Download Tanka today https://www.tanka.ai and enjoy 3 months of free Premium! You can also get $20 / team for each referrals I've been planning for a bitnet video for the longest time, and with the release of bitnet b1.58 2B4T gave me the perfect chance to brief you on the history of 1-bit LLM! Fun fact, the major bitnet research is mostly done by the same researchers. My Newsletter https://mail.bycloud.ai/ my project: find, discover & explain AI research semantically https://findmypapers.ai/ My Patreon https://www.patreon.com/c/bycloud Quantifying the Capabilities of LLMs across Scale and Precision [Paper] https://arxiv.org/abs/2405.03146v2 BitNet: Scaling 1-bit Transformers for Large Language Models [Paper] https://arxiv.org/abs/2310.11453v1 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits [Paper] https://arxiv.org/abs/2402.17764v1 BitNet a4.8: 4-bit Activations for 1-bit LLMs [Paper] https://arxiv.org/abs/2411.04965v1 Efficient Construction of Model Family through Progressive Training Using Model Expansion [Paper] https://arxiv.org/abs/2504.00623v1 BitNet b1.58 2B4T Technical Report [Paper] https://arxiv.org/abs/2504.12285 [Web Demo] https://bitnet-demo.azurewebsites.net/ [HuggingFace] https://huggingface.co/microsoft/bitnet-b1.58-2B-4T [Code] https://github.com/microsoft/BitNet [Additional Recs] T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge https://arxiv.org/abs/2407.00088v2 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation https://arxiv.org/abs/2407.07093v1 Matmul or No Matmul in the Era of 1-bit LLMs https://arxiv.org/abs/2408.11939v2 1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs https://arxiv.org/abs/2410.16144v2 Bitnet.cpp: Efficient Edge Inference for Ternary LLMs https://arxiv.org/abs/2502.11880v1 Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models? https://arxiv.org/abs/2502.11895v1 (NEW!) BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs https://arxiv.org/abs/2504.18415 (NEW!) BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation https://arxiv.org/abs/2506.07530 Try out my new fav place to learn how to code https://scrimba.com/?via=bycloudAI This video is supported by the kind Patrons & YouTube Members: 🙏Nous Research, Chris LeDoux, Ben Shaener, DX Research Group, Poof N' Inu, Andrew Lescelius, Deagan, Robert Zawiasa, Ryszard Warzocha, Tobe2d, Louis Muk, Akkusativ, Kevin Tai, Mark Buckler, NO U, Tony Jimenez, Ângelo Fonseca, jiye, Anushka, Asad Dhamani, Binnie Yiu, Calvin Yan, Clayton Ford, Diego Silva, Etrotta, Gonzalo Fidalgo, Handenon, Hector, Jake Disco very, Michael Brenner, Nilly K, OlegWock, Daddy Wen, Shuhong Chen, Sid_Cipher, Stefan Lorenz, Sup, tantan assawade, Thipok Tham, Thomas Di Martino, Thomas Lin, Richárd Nagyfi, Paperboy, mika, Leo, Berhane-Meskel, Kadhai Pesalam, mayssam, Bill Mangrum, nyaa [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Business Inquiries] bycloud@smoothmedia.co [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] Abhay [Ko-fi] https://ko-fi.com/bycloudai
Content Summary
This report is generated from research on the following videos, based on the requirements set in Video Deep Research.
Analyze selected videos,
My goal is 📑 Discover Content Intelligence
My role is 🎙️ Consultant/Advisor
I need: 🤵 Client demands assessment
https:...4zv0
Summary
1. Optimizing Client Value with Low-Bit AI and Organizational Memory
Knowledge Snap
Metric 1: Resource-Precision Alignment
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Video Title
01:50 - 02:50
A typical GPU with eight gigabytes of memory cannot fit all model weights.
03:25 - 04:25
Using a quantized model is usually better than a smaller model with full precision.
Metric 2: Mathematical Complexity Reduction
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Video Title
06:08 - 07:08
Simple addition and subtraction are enough for models using only two numbers.
07:06 - 08:07
The one bit model uses thirty times less energy than standard parameters.
Metric 3: Information Fragmentation Assessment
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(2)
Video Title
04:03 - 05:03
Scattered messages and buried email threads cause critical context to be lost.
04:37 - 05:38
Structured memory converts scattered documents into a searchable team brain.
Metric 4: Scaling Law Optimization
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(2)
Video Title
08:26 - 09:27
Increasing the number of parameters makes the bit model perform better.
08:35 - 09:36
Scaling a model to seventy billion parameters requires much less memory.
The Evolution of 1-Bit Large Language Models
UCgfe2ooZD3VJPB6aJAnuQng
🏗️
Hardware Accessibility Barriers
00:07 - 01:07
High costs for cutting-edge models create significant barriers for general users and developers.
📉
Downsizing and Distillation Efforts
00:19 - 01:19
Researchers attempt to make models more manageable by reducing size or distilling their knowledge.
✂️
Quantization as an Efficiency Tool
02:04 - 03:05
Lowering precision through quantization helps models fit into limited video memory without massive slowdowns.
🚀
Radical Scaling with 1-Bit Transformers
05:49 - 06:49
New research proposes scaling models using only one bit to drastically reduce storage requirements.
⚡
Massive Energy Savings
07:06 - 08:07
One-bit setups provide energy efficiency gains far beyond initial expectations for standard parameter counts.
➕
Refining Performance with Ternary States
07:29 - 08:29
Introducing a third state allows models to utilize sparsity and improve overall predictive performance.
📊
Benchmarking Against Full Precision
08:02 - 09:02
Advanced 1-bit models can match or outperform larger models while using significantly less memory.
🔮
Optimizing Signal Flow
09:36 - 10:38
Ongoing research aims to further reduce activation precision to squeeze out even more processing efficiency.
Strategic Advisory for Content Intelligence Discovery
| Stage | Videos |
|---|---|
1. Auditing Client Infrastructure Costs | https://www.tanka.ai and enjoy 3 months of free Premium! You can also get $20 / team for each referrals I've been planning for a bitnet video for the longest time, and with the release of bitnet b1.58 2B4T gave me the perfect chance to brief you on the history of 1-bit LLM! Fun fact, the major bitnet research is mostly done by the same researchers. My Newsletter https://mail.bycloud.ai/ my project: find, discover & explain AI research semantically https://findmypapers.ai/ My Patreon https://www.patreon.com/c/bycloud Quantifying the Capabilities of LLMs across Scale and Precision [Paper] https://arxiv.org/abs/2405.03146v2 BitNet: Scaling 1-bit Transformers for Large Language Models [Paper] https://arxiv.org/abs/2310.11453v1 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits [Paper] https://arxiv.org/abs/2402.17764v1 BitNet a4.8: 4-bit Activations for 1-bit LLMs [Paper] https://arxiv.org/abs/2411.04965v1 Efficient Construction of Model Family through Progressive Training Using Model Expansion [Paper] https://arxiv.org/abs/2504.00623v1 BitNet b1.58 2B4T Technical Report [Paper] https://arxiv.org/abs/2504.12285 [Web Demo] https://bitnet-demo.azurewebsites.net/ [HuggingFace] https://huggingface.co/microsoft/bitnet-b1.58-2B-4T [Code] https://github.com/microsoft/BitNet [Additional Recs] T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge https://arxiv.org/abs/2407.00088v2 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation https://arxiv.org/abs/2407.07093v1 Matmul or No Matmul in the Era of 1-bit LLMs https://arxiv.org/abs/2408.11939v2 1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs https://arxiv.org/abs/2410.16144v2 Bitnet.cpp: Efficient Edge Inference for Ternary LLMs https://arxiv.org/abs/2502.11880v1 Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models? https://arxiv.org/abs/2502.11895v1 (NEW!) BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs https://arxiv.org/abs/2504.18415 (NEW!) BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation https://arxiv.org/abs/2506.07530 Try out my new fav place to learn how to code https://scrimba.com/?via=bycloudAI This video is supported by the kind Patrons & YouTube Members: 🙏Nous Research, Chris LeDoux, Ben Shaener, DX Research Group, Poof N' Inu, Andrew Lescelius, Deagan, Robert Zawiasa, Ryszard Warzocha, Tobe2d, Louis Muk, Akkusativ, Kevin Tai, Mark Buckler, NO U, Tony Jimenez, Ângelo Fonseca, jiye, Anushka, Asad Dhamani, Binnie Yiu, Calvin Yan, Clayton Ford, Diego Silva, Etrotta, Gonzalo Fidalgo, Handenon, Hector, Jake Disco very, Michael Brenner, Nilly K, OlegWock, Daddy Wen, Shuhong Chen, Sid_Cipher, Stefan Lorenz, Sup, tantan assawade, Thipok Tham, Thomas Di Martino, Thomas Lin, Richárd Nagyfi, Paperboy, mika, Leo, Berhane-Meskel, Kadhai Pesalam, mayssam, Bill Mangrum, nyaa [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Business Inquiries] bycloud@smoothmedia.co [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] Abhay [Ko-fi] https://ko-fi.com/bycloudai |
2. Mapping Data Footprint and Memory | https://www.tanka.ai and enjoy 3 months of free Premium! You can also get $20 / team for each referrals I've been planning for a bitnet video for the longest time, and with the release of bitnet b1.58 2B4T gave me the perfect chance to brief you on the history of 1-bit LLM! Fun fact, the major bitnet research is mostly done by the same researchers. My Newsletter https://mail.bycloud.ai/ my project: find, discover & explain AI research semantically https://findmypapers.ai/ My Patreon https://www.patreon.com/c/bycloud Quantifying the Capabilities of LLMs across Scale and Precision [Paper] https://arxiv.org/abs/2405.03146v2 BitNet: Scaling 1-bit Transformers for Large Language Models [Paper] https://arxiv.org/abs/2310.11453v1 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits [Paper] https://arxiv.org/abs/2402.17764v1 BitNet a4.8: 4-bit Activations for 1-bit LLMs [Paper] https://arxiv.org/abs/2411.04965v1 Efficient Construction of Model Family through Progressive Training Using Model Expansion [Paper] https://arxiv.org/abs/2504.00623v1 BitNet b1.58 2B4T Technical Report [Paper] https://arxiv.org/abs/2504.12285 [Web Demo] https://bitnet-demo.azurewebsites.net/ [HuggingFace] https://huggingface.co/microsoft/bitnet-b1.58-2B-4T [Code] https://github.com/microsoft/BitNet [Additional Recs] T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge https://arxiv.org/abs/2407.00088v2 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation https://arxiv.org/abs/2407.07093v1 Matmul or No Matmul in the Era of 1-bit LLMs https://arxiv.org/abs/2408.11939v2 1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs https://arxiv.org/abs/2410.16144v2 Bitnet.cpp: Efficient Edge Inference for Ternary LLMs https://arxiv.org/abs/2502.11880v1 Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models? https://arxiv.org/abs/2502.11895v1 (NEW!) BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs https://arxiv.org/abs/2504.18415 (NEW!) BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation https://arxiv.org/abs/2506.07530 Try out my new fav place to learn how to code https://scrimba.com/?via=bycloudAI This video is supported by the kind Patrons & YouTube Members: 🙏Nous Research, Chris LeDoux, Ben Shaener, DX Research Group, Poof N' Inu, Andrew Lescelius, Deagan, Robert Zawiasa, Ryszard Warzocha, Tobe2d, Louis Muk, Akkusativ, Kevin Tai, Mark Buckler, NO U, Tony Jimenez, Ângelo Fonseca, jiye, Anushka, Asad Dhamani, Binnie Yiu, Calvin Yan, Clayton Ford, Diego Silva, Etrotta, Gonzalo Fidalgo, Handenon, Hector, Jake Disco very, Michael Brenner, Nilly K, OlegWock, Daddy Wen, Shuhong Chen, Sid_Cipher, Stefan Lorenz, Sup, tantan assawade, Thipok Tham, Thomas Di Martino, Thomas Lin, Richárd Nagyfi, Paperboy, mika, Leo, Berhane-Meskel, Kadhai Pesalam, mayssam, Bill Mangrum, nyaa [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Business Inquiries] bycloud@smoothmedia.co [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] Abhay [Ko-fi] https://ko-fi.com/bycloudai |
3. Navigating Precision and Accuracy Limits | https://www.tanka.ai and enjoy 3 months of free Premium! You can also get $20 / team for each referrals I've been planning for a bitnet video for the longest time, and with the release of bitnet b1.58 2B4T gave me the perfect chance to brief you on the history of 1-bit LLM! Fun fact, the major bitnet research is mostly done by the same researchers. My Newsletter https://mail.bycloud.ai/ my project: find, discover & explain AI research semantically https://findmypapers.ai/ My Patreon https://www.patreon.com/c/bycloud Quantifying the Capabilities of LLMs across Scale and Precision [Paper] https://arxiv.org/abs/2405.03146v2 BitNet: Scaling 1-bit Transformers for Large Language Models [Paper] https://arxiv.org/abs/2310.11453v1 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits [Paper] https://arxiv.org/abs/2402.17764v1 BitNet a4.8: 4-bit Activations for 1-bit LLMs [Paper] https://arxiv.org/abs/2411.04965v1 Efficient Construction of Model Family through Progressive Training Using Model Expansion [Paper] https://arxiv.org/abs/2504.00623v1 BitNet b1.58 2B4T Technical Report [Paper] https://arxiv.org/abs/2504.12285 [Web Demo] https://bitnet-demo.azurewebsites.net/ [HuggingFace] https://huggingface.co/microsoft/bitnet-b1.58-2B-4T [Code] https://github.com/microsoft/BitNet [Additional Recs] T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge https://arxiv.org/abs/2407.00088v2 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation https://arxiv.org/abs/2407.07093v1 Matmul or No Matmul in the Era of 1-bit LLMs https://arxiv.org/abs/2408.11939v2 1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs https://arxiv.org/abs/2410.16144v2 Bitnet.cpp: Efficient Edge Inference for Ternary LLMs https://arxiv.org/abs/2502.11880v1 Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models? https://arxiv.org/abs/2502.11895v1 (NEW!) BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs https://arxiv.org/abs/2504.18415 (NEW!) BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation https://arxiv.org/abs/2506.07530 Try out my new fav place to learn how to code https://scrimba.com/?via=bycloudAI This video is supported by the kind Patrons & YouTube Members: 🙏Nous Research, Chris LeDoux, Ben Shaener, DX Research Group, Poof N' Inu, Andrew Lescelius, Deagan, Robert Zawiasa, Ryszard Warzocha, Tobe2d, Louis Muk, Akkusativ, Kevin Tai, Mark Buckler, NO U, Tony Jimenez, Ângelo Fonseca, jiye, Anushka, Asad Dhamani, Binnie Yiu, Calvin Yan, Clayton Ford, Diego Silva, Etrotta, Gonzalo Fidalgo, Handenon, Hector, Jake Disco very, Michael Brenner, Nilly K, OlegWock, Daddy Wen, Shuhong Chen, Sid_Cipher, Stefan Lorenz, Sup, tantan assawade, Thipok Tham, Thomas Di Martino, Thomas Lin, Richárd Nagyfi, Paperboy, mika, Leo, Berhane-Meskel, Kadhai Pesalam, mayssam, Bill Mangrum, nyaa [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Business Inquiries] bycloud@smoothmedia.co [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] Abhay [Ko-fi] https://ko-fi.com/bycloudai |
4. Ground-Up Architectural Strategy | https://www.tanka.ai and enjoy 3 months of free Premium! You can also get $20 / team for each referrals I've been planning for a bitnet video for the longest time, and with the release of bitnet b1.58 2B4T gave me the perfect chance to brief you on the history of 1-bit LLM! Fun fact, the major bitnet research is mostly done by the same researchers. My Newsletter https://mail.bycloud.ai/ my project: find, discover & explain AI research semantically https://findmypapers.ai/ My Patreon https://www.patreon.com/c/bycloud Quantifying the Capabilities of LLMs across Scale and Precision [Paper] https://arxiv.org/abs/2405.03146v2 BitNet: Scaling 1-bit Transformers for Large Language Models [Paper] https://arxiv.org/abs/2310.11453v1 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits [Paper] https://arxiv.org/abs/2402.17764v1 BitNet a4.8: 4-bit Activations for 1-bit LLMs [Paper] https://arxiv.org/abs/2411.04965v1 Efficient Construction of Model Family through Progressive Training Using Model Expansion [Paper] https://arxiv.org/abs/2504.00623v1 BitNet b1.58 2B4T Technical Report [Paper] https://arxiv.org/abs/2504.12285 [Web Demo] https://bitnet-demo.azurewebsites.net/ [HuggingFace] https://huggingface.co/microsoft/bitnet-b1.58-2B-4T [Code] https://github.com/microsoft/BitNet [Additional Recs] T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge https://arxiv.org/abs/2407.00088v2 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation https://arxiv.org/abs/2407.07093v1 Matmul or No Matmul in the Era of 1-bit LLMs https://arxiv.org/abs/2408.11939v2 1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs https://arxiv.org/abs/2410.16144v2 Bitnet.cpp: Efficient Edge Inference for Ternary LLMs https://arxiv.org/abs/2502.11880v1 Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models? https://arxiv.org/abs/2502.11895v1 (NEW!) BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs https://arxiv.org/abs/2504.18415 (NEW!) BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation https://arxiv.org/abs/2506.07530 Try out my new fav place to learn how to code https://scrimba.com/?via=bycloudAI This video is supported by the kind Patrons & YouTube Members: 🙏Nous Research, Chris LeDoux, Ben Shaener, DX Research Group, Poof N' Inu, Andrew Lescelius, Deagan, Robert Zawiasa, Ryszard Warzocha, Tobe2d, Louis Muk, Akkusativ, Kevin Tai, Mark Buckler, NO U, Tony Jimenez, Ângelo Fonseca, jiye, Anushka, Asad Dhamani, Binnie Yiu, Calvin Yan, Clayton Ford, Diego Silva, Etrotta, Gonzalo Fidalgo, Handenon, Hector, Jake Disco very, Michael Brenner, Nilly K, OlegWock, Daddy Wen, Shuhong Chen, Sid_Cipher, Stefan Lorenz, Sup, tantan assawade, Thipok Tham, Thomas Di Martino, Thomas Lin, Richárd Nagyfi, Paperboy, mika, Leo, Berhane-Meskel, Kadhai Pesalam, mayssam, Bill Mangrum, nyaa [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Business Inquiries] bycloud@smoothmedia.co [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] Abhay [Ko-fi] https://ko-fi.com/bycloudai |
5. Quantifying Operational Efficiency Gains | https://www.tanka.ai and enjoy 3 months of free Premium! You can also get $20 / team for each referrals I've been planning for a bitnet video for the longest time, and with the release of bitnet b1.58 2B4T gave me the perfect chance to brief you on the history of 1-bit LLM! Fun fact, the major bitnet research is mostly done by the same researchers. My Newsletter https://mail.bycloud.ai/ my project: find, discover & explain AI research semantically https://findmypapers.ai/ My Patreon https://www.patreon.com/c/bycloud Quantifying the Capabilities of LLMs across Scale and Precision [Paper] https://arxiv.org/abs/2405.03146v2 BitNet: Scaling 1-bit Transformers for Large Language Models [Paper] https://arxiv.org/abs/2310.11453v1 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits [Paper] https://arxiv.org/abs/2402.17764v1 BitNet a4.8: 4-bit Activations for 1-bit LLMs [Paper] https://arxiv.org/abs/2411.04965v1 Efficient Construction of Model Family through Progressive Training Using Model Expansion [Paper] https://arxiv.org/abs/2504.00623v1 BitNet b1.58 2B4T Technical Report [Paper] https://arxiv.org/abs/2504.12285 [Web Demo] https://bitnet-demo.azurewebsites.net/ [HuggingFace] https://huggingface.co/microsoft/bitnet-b1.58-2B-4T [Code] https://github.com/microsoft/BitNet [Additional Recs] T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge https://arxiv.org/abs/2407.00088v2 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation https://arxiv.org/abs/2407.07093v1 Matmul or No Matmul in the Era of 1-bit LLMs https://arxiv.org/abs/2408.11939v2 1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs https://arxiv.org/abs/2410.16144v2 Bitnet.cpp: Efficient Edge Inference for Ternary LLMs https://arxiv.org/abs/2502.11880v1 Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models? https://arxiv.org/abs/2502.11895v1 (NEW!) BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs https://arxiv.org/abs/2504.18415 (NEW!) BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation https://arxiv.org/abs/2506.07530 Try out my new fav place to learn how to code https://scrimba.com/?via=bycloudAI This video is supported by the kind Patrons & YouTube Members: 🙏Nous Research, Chris LeDoux, Ben Shaener, DX Research Group, Poof N' Inu, Andrew Lescelius, Deagan, Robert Zawiasa, Ryszard Warzocha, Tobe2d, Louis Muk, Akkusativ, Kevin Tai, Mark Buckler, NO U, Tony Jimenez, Ângelo Fonseca, jiye, Anushka, Asad Dhamani, Binnie Yiu, Calvin Yan, Clayton Ford, Diego Silva, Etrotta, Gonzalo Fidalgo, Handenon, Hector, Jake Disco very, Michael Brenner, Nilly K, OlegWock, Daddy Wen, Shuhong Chen, Sid_Cipher, Stefan Lorenz, Sup, tantan assawade, Thipok Tham, Thomas Di Martino, Thomas Lin, Richárd Nagyfi, Paperboy, mika, Leo, Berhane-Meskel, Kadhai Pesalam, mayssam, Bill Mangrum, nyaa [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Business Inquiries] bycloud@smoothmedia.co [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] Abhay [Ko-fi] https://ko-fi.com/bycloudai |
Detailed Findings and Insights
1. The Dead Signal Constraint
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Video Title
07:24 - 08:24
Dead signals are just as important as active ones in model communication.
Transcription
sometimes dead signals are also as
2. Persistent Activation Bottlenecks
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(1)
Video Title
10:08 - 11:09
The KV cache creates a bottleneck that increases with the size of the context.
Transcription
context window, KV cache can easily
3. Outlier Precision Necessity
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Video Title
09:53 - 10:54
Specific data distributions often contain important outlier values for model accuracy.
Transcription
usually include very important outlier
4. Representation Stability Factors
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Video Title
06:41 - 07:42
Building representations from the ground up provides much better stability.
Transcription
representations from the ground up. So
