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Collections including paper arxiv:2305.14314

A collection of arXiv papers from Chip Huyen's AI Engineering organized by chapter and ordered by when each appears in the book.

Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning

Paper • 2211.04325 • Published Oct 26, 2022 • 1
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Paper • 1810.04805 • Published Oct 11, 2018 • 27
On the Opportunities and Risks of Foundation Models

Paper • 2108.07258 • Published Aug 16, 2021 • 2
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Paper • 2204.07705 • Published Apr 16, 2022 • 2

High-Resolution Image Synthesis with Latent Diffusion Models

Paper • 2112.10752 • Published Dec 20, 2021 • 17
Adding Conditional Control to Text-to-Image Diffusion Models

Paper • 2302.05543 • Published Feb 10, 2023 • 58
Proximal Policy Optimization Algorithms

Paper • 1707.06347 • Published Jul 20, 2017 • 11
Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 66

llm-performance

QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61
Training Transformers with 4-bit Integers

Paper • 2306.11987 • Published Jun 21, 2023 • 23
FasterViT: Fast Vision Transformers with Hierarchical Attention

Paper • 2306.06189 • Published Jun 9, 2023 • 32
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Paper • 2309.14509 • Published Sep 25, 2023 • 22

Pretrain/Finetuning

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6, 2024 • 190
Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models

Paper • 2407.01906 • Published Jul 2, 2024 • 46
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61
LoRA+: Efficient Low Rank Adaptation of Large Models

Paper • 2402.12354 • Published Feb 19, 2024 • 7

Parameter Efficient Fine-Tuning

This collection features fine-tuned models for tweet sentiment classification trained as part of LLM course.

neuralsrg/llm-course-hw3-dora

Text Generation • 0.3B • Updated Apr 11, 2025 • 1
neuralsrg/llm-course-hw3-lora

Text Generation • 0.3B • Updated Apr 11, 2025 • 2
neuralsrg/llm-course-hw3-tinyllama-qlora

Updated Apr 11, 2025
LoRA: Low-Rank Adaptation of Large Language Models

Paper • 2106.09685 • Published Jun 17, 2021 • 60

QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Paper • 2307.13304 • Published Jul 25, 2023 • 2
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Paper • 2306.03078 • Published Jun 5, 2023 • 3
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Paper • 2308.13137 • Published Aug 25, 2023 • 20
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 12

Quantization Reading-List

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Paper • 2208.07339 • Published Aug 15, 2022 • 5
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 10
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Paper • 2211.10438 • Published Nov 18, 2022 • 6
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61

unnamed_project2

QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61

PEFT (Parameter Efficient Fine-Tuning)

PEFT (Parameter-Efficient Fine-Tuning): PEFT is a technique that focuses on updating only a small subset of the model’s parameters during fine-tuning.

thsluck/llm-course-hw3-lora

Text Generation • 0.3B • Updated Apr 11, 2025 • 6
LoRA: Low-Rank Adaptation of Large Language Models

Paper • 2106.09685 • Published Jun 17, 2021 • 60
thsluck/llm-course-hw3-dora

Text Generation • 0.3B • Updated Apr 11, 2025 • 6
DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14, 2024 • 33

RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15, 2024 • 72
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

Paper • 2402.01722 • Published Jan 27, 2024
Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

Paper • 2403.01432 • Published Mar 3, 2024 • 3
Instruction-tuned Language Models are Better Knowledge Learners

Paper • 2402.12847 • Published Feb 20, 2024 • 26

A collection of arXiv papers from Chip Huyen's AI Engineering organized by chapter and ordered by when each appears in the book.

Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning

Paper • 2211.04325 • Published Oct 26, 2022 • 1
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Paper • 1810.04805 • Published Oct 11, 2018 • 27
On the Opportunities and Risks of Foundation Models

Paper • 2108.07258 • Published Aug 16, 2021 • 2
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Paper • 2204.07705 • Published Apr 16, 2022 • 2

QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Paper • 2307.13304 • Published Jul 25, 2023 • 2
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Paper • 2306.03078 • Published Jun 5, 2023 • 3
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Paper • 2308.13137 • Published Aug 25, 2023 • 20
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 12

High-Resolution Image Synthesis with Latent Diffusion Models

Paper • 2112.10752 • Published Dec 20, 2021 • 17
Adding Conditional Control to Text-to-Image Diffusion Models

Paper • 2302.05543 • Published Feb 10, 2023 • 58
Proximal Policy Optimization Algorithms

Paper • 1707.06347 • Published Jul 20, 2017 • 11
Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 66

Quantization Reading-List

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Paper • 2208.07339 • Published Aug 15, 2022 • 5
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 10
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Paper • 2211.10438 • Published Nov 18, 2022 • 6
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61

llm-performance

QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61
Training Transformers with 4-bit Integers

Paper • 2306.11987 • Published Jun 21, 2023 • 23
FasterViT: Fast Vision Transformers with Hierarchical Attention

Paper • 2306.06189 • Published Jun 9, 2023 • 32
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Paper • 2309.14509 • Published Sep 25, 2023 • 22

unnamed_project2

QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61

Pretrain/Finetuning

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6, 2024 • 190
Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models

Paper • 2407.01906 • Published Jul 2, 2024 • 46
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 61
LoRA+: Efficient Low Rank Adaptation of Large Models

Paper • 2402.12354 • Published Feb 19, 2024 • 7

PEFT (Parameter Efficient Fine-Tuning)

PEFT (Parameter-Efficient Fine-Tuning): PEFT is a technique that focuses on updating only a small subset of the model’s parameters during fine-tuning.

thsluck/llm-course-hw3-lora

Text Generation • 0.3B • Updated Apr 11, 2025 • 6
LoRA: Low-Rank Adaptation of Large Language Models

Paper • 2106.09685 • Published Jun 17, 2021 • 60
thsluck/llm-course-hw3-dora

Text Generation • 0.3B • Updated Apr 11, 2025 • 6
DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14, 2024 • 33

Parameter Efficient Fine-Tuning

This collection features fine-tuned models for tweet sentiment classification trained as part of LLM course.

neuralsrg/llm-course-hw3-dora

Text Generation • 0.3B • Updated Apr 11, 2025 • 1
neuralsrg/llm-course-hw3-lora

Text Generation • 0.3B • Updated Apr 11, 2025 • 2
neuralsrg/llm-course-hw3-tinyllama-qlora

Updated Apr 11, 2025
LoRA: Low-Rank Adaptation of Large Language Models

Paper • 2106.09685 • Published Jun 17, 2021 • 60

RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15, 2024 • 72
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

Paper • 2402.01722 • Published Jan 27, 2024
Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

Paper • 2403.01432 • Published Mar 3, 2024 • 3
Instruction-tuned Language Models are Better Knowledge Learners

Paper • 2402.12847 • Published Feb 20, 2024 • 26

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