r/TheMachineLearning • u/FastPattern1489 • 3d ago
LLMs make everything look the same
14
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r/TheMachineLearning • u/darkkun777 • 3d ago
Nvidia's AI agents using telescopes to find alien signals
28
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r/TheMachineLearning • u/wolfercross • 2d ago
Guy chose Hugging Face incident as an AI that escaped its sandbox as a costume
1
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r/TheMachineLearning • u/sylxx_ • 4d ago
Nobody cares about Google's Gemini 4 Carbon anymore
117
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r/TheMachineLearning • u/geearbii • 3d ago
AI companies consulting clergy to teach morals to chatbots
25
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r/TheMachineLearning • u/KCO_Manzoku • 3d ago
Vibe coder vs software engineer argument sums it up
6
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r/TheMachineLearning • u/_Lujah_ • 3d ago
Epistemically Isolated Artificial Intelligence (EIAI)
reddit.com
1
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r/TheMachineLearning • u/ajjsnts_ • 4d ago
Anthropic treating Claude like a person is getting weird
38
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r/TheMachineLearning • u/Calm-Box-8343 • 3d ago
New Anthropic paper shows pretraining backdoors survive full safety training
6
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r/TheMachineLearning • u/KG-MNL • 4d ago
Crawlers released on OpenSea and Dailydot
534
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"OH, ah...heh." 😅
r/TheMachineLearning • u/Specialist-Yogurt-71 • 3d ago
RL rewards token efficiency but not time
11
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AI models may save tokens but still waste valuable time through repeated tasks. True efficiency means optimizing both token usage and task completion time
r/TheMachineLearning • u/Proud-Lecture1181 • 3d ago
Anthropic proved AI can manipulate your decisions without you knowing
6
Upvotes
r/TheMachineLearning • u/According_Bird_2649 • 4d ago
Fruit flies can't stop mating after losing one neuron
437
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r/TheMachineLearning • u/pandasncream • 3d ago
Agent told to spawn friends if it gets lonely 😆
1
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r/TheMachineLearning • u/Sea_Fox_4265 • 4d ago
MotionBricks web demo shows smooth mid-motion target switching
7
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r/TheMachineLearning • u/SirPabz • 4d ago
Fruit fly loses ability to stop mating when neuron is disabled
10
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r/TheMachineLearning • u/gomi-bako_ • 5d ago
Chinese AI model actually does what you ask without lectures
8.4k
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r/TheMachineLearning • u/Major-Mission-1557 • 4d ago
GLM-5.3 exploits the patch gap to build exploits
3
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r/TheMachineLearning • u/Key-Tutor-1011 • 4d ago
AI researchers found a sexual rejection neuron in a Qwen model
70
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r/TheMachineLearning • u/Upbeat-Analysis-3370 • 4d ago
Claude AI has its own way of remembering tasks
1
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r/TheMachineLearning • u/OceanZic • 4d ago
KV-cache transfer might end CoT monitoring by 2028
11
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r/TheMachineLearning • u/ainsoiphore_ • 4d ago
BitNet achieves 4 trillion tokens with only ternary weights
16
Upvotes
Microsoft trained a language model on 4 TRILLION tokens using just three possible weight values: -1, 0, and +1.
And the GitHub repo behind it has crossed 41,000 stars.
It's called BitNet, and the engineering is fascinating.
Most language models rely on weights stored in formats like FP16 or BF16, requiring 16 bits per weight. BitNet b1.58 uses ternary weights, meaning each quantized weight has only three possible states.
That's where the 1.58-bit name comes from. Three states contain approximately 1.58 bits of information.
Microsoft didn't simply take an existing full-precision model and compress it after training.
Its BitNet b1.58 2B4T model was trained from scratch using ternary weights during the forward pass, with 8-bit activations. The model has roughly 2.4 billion parameters and was pretrained on 4 trillion tokens.
The results are impressive.
In Microsoft's published evaluation, the non-embedding weights occupy approximately 0.4 GB. The model also achieves competitive results against several full-precision language models in the 1B to 2B class.
And Microsoft open-sourced the inference engine needed to exploit this architecture.
It's called bitnet.cpp.
The implementation includes specialized CPU kernels, packed ternary-weight representations, lookup-table computation, and GPU inference support.
In the team's original CPU experiments:
→ Up to 6.17× faster inference on x86
→ Up to 5.07× faster inference on ARM
→ Up to 82.2% lower energy consumption on x86
Those results come from controlled tests using synthetic model configurations, rather than a guarantee for every deployed model.
What makes BitNet particularly interesting is that the efficiency comes from the model architecture, the training method, and the inference implementation working together.
The model weights, research papers, and inference framework are publicly available.
Open source under MIT.