Thinking LLMs: General Instruction Following with Thought Generation
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Posts by Tag
- LLMs 28
- efficient-inference 14
- training-dynamics 8
- efficient-training 7
- quantisation 7
- transformers 6
- computer-vision 4
- fine-tuning 4
- GNNs 3
- diffusion 3
- mixture-of-experts 3
- sparsity 3
- mup 3
- unit-scaling 2
- materials 2
- scaling-laws 2
- inference 2
- long-context 2
- position-embeddings 2
- speculative-decoding 2
- retrieval-augmented-generation 2
- sparse-attention 2
- not-transformers 2
- self-correction 2
- image-generation 2
- chip-design 1
- fp8 1
- number-formats 1
- DFT 1
- active-learning 1
- multi-modality 1
- automated-theorem-proving 1
- synthetic-data 1
- distributed-training 1
- local-updates 1
- optimization 1
- learning-rate-schedules 1
- RNNs 1
- state-space-models 1
- hallucinations 1
- reinforcement-learning 1
- optimisation 1
- hiring 1
- reasoning 1
LLMs
Generative Hierarchical Materials Search
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Speeding up LLM inference using SparQ Attention & llama.cpp
With the rapid advances in the capabilities of large language models (LLMs), there is an increasing need for efficient inference platforms that would enable ...
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability
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Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
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Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach
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Scalable MatMul-free Language Modeling
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xLSTM: Extended Long Short-Term Memory
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TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
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QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
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A transformer walk-through, with Gemma
Transformer-based LLMs seem mysterious, but they don’t need to. In this post, we’ll walk through a modern transformer LLM, Google’s Gemma, providing bare-bon...
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
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FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design
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Massive Activations in Large Language Models
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Simplifying Transformer Blocks
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ChipNeMo: Domain-Adapted LLMs for Chip Design
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efficient-inference
Scaling FP8 training to trillion-token LLMs
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Speeding up LLM inference using SparQ Attention & llama.cpp
With the rapid advances in the capabilities of large language models (LLMs), there is an increasing need for efficient inference platforms that would enable ...
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
The key idea
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach
The key idea
Scalable MatMul-free Language Modeling
The key idea
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
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TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
The key idea
QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
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training-dynamics
Scale-preserving nonlinearities for u-μP
My colleagues and I always get excited when, every once in a while, deep learning research throws up a fun little maths problem. Our recent work on u-μP does...
Sparse maximal update parameterization: A holistic approach to sparse training dynamics
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The Road Less Scheduled
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Massive Activations in Large Language Models
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Simplifying Transformer Blocks
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Almost-scaled dot-product attention
TL;DR: Scaled dot product attention isn’t properly scaled, and that’s a good thing!
efficient-training
Mixture of a Million Experts
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quantisation
Scaling FP8 training to trillion-token LLMs
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Scalable MatMul-free Language Modeling
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QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
The key idea
transformers
Speeding up LLM inference using SparQ Attention & llama.cpp
With the rapid advances in the capabilities of large language models (LLMs), there is an increasing need for efficient inference platforms that would enable ...
A transformer walk-through, with Gemma
Transformer-based LLMs seem mysterious, but they don’t need to. In this post, we’ll walk through a modern transformer LLM, Google’s Gemma, providing bare-bon...
Massive Activations in Large Language Models
The key idea
Simplifying Transformer Blocks
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computer-vision
fine-tuning
GNNs
Generative Hierarchical Materials Search
The key idea
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
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Scaling Deep Learning for Materials Discovery
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diffusion
Generative Hierarchical Materials Search
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mixture-of-experts
Mixture of a Million Experts
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DiPaCo: Distributed Path Composition
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sparsity
Speeding up LLM inference using SparQ Attention & llama.cpp
With the rapid advances in the capabilities of large language models (LLMs), there is an increasing need for efficient inference platforms that would enable ...
Mixture of a Million Experts
The key idea
mup
Scale-preserving nonlinearities for u-μP
My colleagues and I always get excited when, every once in a while, deep learning research throws up a fun little maths problem. Our recent work on u-μP does...
unit-scaling
Scale-preserving nonlinearities for u-μP
My colleagues and I always get excited when, every once in a while, deep learning research throws up a fun little maths problem. Our recent work on u-μP does...
Almost-scaled dot-product attention
TL;DR: Scaled dot product attention isn’t properly scaled, and that’s a good thing!
materials
Generative Hierarchical Materials Search
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Scaling Deep Learning for Materials Discovery
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scaling-laws
inference
long-context
position-embeddings
speculative-decoding
retrieval-augmented-generation
sparse-attention
Speeding up LLM inference using SparQ Attention & llama.cpp
With the rapid advances in the capabilities of large language models (LLMs), there is an increasing need for efficient inference platforms that would enable ...
not-transformers
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
The key idea
xLSTM: Extended Long Short-Term Memory
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self-correction
image-generation
chip-design
ChipNeMo: Domain-Adapted LLMs for Chip Design
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fp8
FP8-LM: Training FP8 Large Language Models
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number-formats
FP8-LM: Training FP8 Large Language Models
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DFT
Scaling Deep Learning for Materials Discovery
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active-learning
multi-modality
automated-theorem-proving
Back to Top ↑synthetic-data
Back to Top ↑distributed-training
DiPaCo: Distributed Path Composition
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local-updates
DiPaCo: Distributed Path Composition
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optimization
The Road Less Scheduled
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learning-rate-schedules
The Road Less Scheduled
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RNNs
xLSTM: Extended Long Short-Term Memory
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state-space-models
hallucinations
reinforcement-learning
Back to Top ↑optimisation
SOAP: Improving and Stabilizing Shampoo using Adam
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hiring
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We are pleased to have announce we have open positions for Research Scientists and Engineers to join our team.