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Collections including paper arxiv:2210.17323

article collection

EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning

Paper • 2509.22576 • Published Sep 26 • 133
AgentBench: Evaluating LLMs as Agents

Paper • 2308.03688 • Published Aug 7, 2023 • 25
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Paper • 1910.01108 • Published Oct 2, 2019 • 21
Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 63

Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models

Paper • 2504.04823 • Published Apr 7 • 31
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11
The case for 4-bit precision: k-bit Inference Scaling Laws

Paper • 2212.09720 • Published Dec 19, 2022 • 3

Quantization Papers

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27, 2024 • 625

🔧 Optimization Mechanics 🔧

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Paper • 2208.07339 • Published Aug 15, 2022 • 5
Hydragen: High-Throughput LLM Inference with Shared Prefixes

Paper • 2402.05099 • Published Feb 7, 2024 • 20
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Paper • 2401.10774 • Published Jan 19, 2024 • 59

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 • 18
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11

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 • 8
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 • 57

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Paper • 2407.11062 • Published Jul 10, 2024 • 10
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11

Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 96
LLaMA: Open and Efficient Foundation Language Models

Paper • 2302.13971 • Published Feb 27, 2023 • 18
Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30, 2024 • 21
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts

Paper • 2407.21770 • Published Jul 31, 2024 • 22

A little guide to building Large Language Models in 2024

Resources mentioned by @thomwolf in https://x.com/Thom_Wolf/status/1773340316835131757

Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7, 2024 • 65
A Survey on Data Selection for Language Models

Paper • 2402.16827 • Published Feb 26, 2024 • 4
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Paper • 2402.00159 • Published Jan 31, 2024 • 65
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Paper • 2306.01116 • Published Jun 1, 2023 • 41

Papers: Quantization

FP8-LM: Training FP8 Large Language Models

Paper • 2310.18313 • Published Oct 27, 2023 • 33
LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 14
TEQ: Trainable Equivalent Transformation for Quantization of LLMs

Paper • 2310.10944 • Published Oct 17, 2023 • 10
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers

Paper • 2309.16119 • Published Sep 28, 2023 • 1

article collection

EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning

Paper • 2509.22576 • Published Sep 26 • 133
AgentBench: Evaluating LLMs as Agents

Paper • 2308.03688 • Published Aug 7, 2023 • 25
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Paper • 1910.01108 • Published Oct 2, 2019 • 21
Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 63

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 • 8
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 • 57

Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models

Paper • 2504.04823 • Published Apr 7 • 31
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11
The case for 4-bit precision: k-bit Inference Scaling Laws

Paper • 2212.09720 • Published Dec 19, 2022 • 3

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Paper • 2407.11062 • Published Jul 10, 2024 • 10
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11

Quantization Papers

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27, 2024 • 625

Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 96
LLaMA: Open and Efficient Foundation Language Models

Paper • 2302.13971 • Published Feb 27, 2023 • 18
Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30, 2024 • 21
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts

Paper • 2407.21770 • Published Jul 31, 2024 • 22

🔧 Optimization Mechanics 🔧

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 8
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Paper • 2208.07339 • Published Aug 15, 2022 • 5
Hydragen: High-Throughput LLM Inference with Shared Prefixes

Paper • 2402.05099 • Published Feb 7, 2024 • 20
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Paper • 2401.10774 • Published Jan 19, 2024 • 59

A little guide to building Large Language Models in 2024

Resources mentioned by @thomwolf in https://x.com/Thom_Wolf/status/1773340316835131757

Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7, 2024 • 65
A Survey on Data Selection for Language Models

Paper • 2402.16827 • Published Feb 26, 2024 • 4
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Paper • 2402.00159 • Published Jan 31, 2024 • 65
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Paper • 2306.01116 • Published Jun 1, 2023 • 41

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 • 18
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 11

Papers: Quantization

FP8-LM: Training FP8 Large Language Models

Paper • 2310.18313 • Published Oct 27, 2023 • 33
LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 14
TEQ: Trainable Equivalent Transformation for Quantization of LLMs

Paper • 2310.10944 • Published Oct 17, 2023 • 10
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers

Paper • 2309.16119 • Published Sep 28, 2023 • 1

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