AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment | Paper Citation Record

Observation 7ac2c22a-3db1-4391-b9e4-b2faa01fe961 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 1

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Observation 3a050736-faf3-42dd-af31-04a86d07851f · outbound

This paper cites Introducing Meta Llama 3: The most capable openly available LLM to date, 2024.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Introducing Meta Llama 3: The most capable openly available LLM to date, 2024

Reference 2

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Observation 4af90698-19e9-4c2d-b08c-d9bcc438bc60 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Gemma 2: Improving Open Language Models at a Practical Size

Reference 3

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Observation 33cffe53-7a20-4394-b8c2-81a4d4a040de · outbound

This paper cites GPT-4 Technical Report.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment GPT-4 Technical Report

Reference 4

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Observation e39b3ff4-9a02-45c0-aeb8-91d111890281 · outbound

This paper cites SparseGPT: Massive language models can be accurately pruned in one-shot, 2023.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment SparseGPT: Massive language models can be accurately pruned in one-shot, 2023

Reference 5

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Observation 074d9850-6d45-47ac-afa7-87d5fa80a25c · outbound

This paper cites A simple and effective pruning approach for large language models, 2023.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment A simple and effective pruning approach for large language models, 2023

Reference 6

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Observation ad6d53fb-7e62-4ac8-b6a7-51c0daa62415 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Llm-pruner: On the structural pruning of large language models

Reference 7

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Observation 6e89a609-dcc2-491c-8720-5ca2b7947cae · outbound

This paper cites Fluctuation-based adaptive structured pruning for large language models.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Fluctuation-based adaptive structured pruning for large language models

Reference 8

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Observation 49e3ff13-fe7c-44d7-8c7f-f67d75b71927 · outbound

This paper cites Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods

Reference 9

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Observation 912d186c-7927-4131-97bc-9acd9026720a · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 10

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Observation 21254411-3deb-4f1a-897e-846df2ca4c09 · outbound

This paper cites Bignas: Scaling up neural architecture search with big single-stage models.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Bignas: Scaling up neural architecture search with big single-stage models

Reference 11

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Observation 918895aa-2039-461c-b132-04d9e7021f1a · outbound

This paper cites Attentivenas: Improving neural architecture search via attentive sampling.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Attentivenas: Improving neural architecture search via attentive sampling

Reference 12

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Observation 8ffcdd17-8bb3-4407-a790-b3f4b800c2c6 · outbound

This paper cites Alphanet: Improved training of supernets with alpha-divergence.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Alphanet: Improved training of supernets with alpha-divergence

Reference 13

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Observation 09e77131-0bb1-4ea7-a568-fee8226a24c7 · outbound

This paper cites Nasvit: Neural architecture search for efficient vision transformers with gradient conflict-aware supernet training.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Nasvit: Neural architecture search for efficient vision transformers with gradient conflict-aware supernet training

Reference 14

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Observation e7171d6d-3dc5-4d10-b95b-6203468d0ef0 · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 15

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Observation 99092a33-0125-4b28-b0c0-989611a220fd · outbound

This paper cites Gradient surgery for multi-task learning.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Gradient surgery for multi-task learning

Reference 16

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This paper cites Conflict-averse gradient descent for multi-task learning.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Conflict-averse gradient descent for multi-task learning

Reference 17

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Observation 2a24467d-a2fa-4b9c-a123-16d08bb7ce07 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

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Observation 6e2a80fe-defc-43da-81d3-5dc364a2e3a8 · outbound

This paper cites Tensorrt-llm, 2024.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Tensorrt-llm, 2024

Reference 19

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Observation 72be604c-366d-497e-919f-3598ab8f8f73 · outbound

This paper cites MLC-LLM, 2023.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment MLC-LLM, 2023

Reference 20

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Pytorch: An imperative style, high-performance deep learning library

Reference 21

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Observation d3468837-168b-4cd9-b4c3-f073a01e5a3e · outbound

This paper cites The theory of dynamic programming.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment The theory of dynamic programming

Reference 22

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Observation 31f05847-ef84-494d-971d-d9e1782e620e · outbound

This paper cites Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Reference 23

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This paper cites A Comprehensive Study of Knowledge Editing for Large Language Models.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment A Comprehensive Study of Knowledge Editing for Large Language Models

Reference 24

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This paper cites Knowledge Neurons in Pretrained Transformers.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Knowledge Neurons in Pretrained Transformers

Reference 25

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This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Transformer Feed-Forward Layers Are Key-Value Memories

Reference 26

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This paper cites What does bert learn about the structure of language? In ACL 2019-57th Annual Meeting of the Association for Computational Linguistics, 2019.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment What does bert learn about the structure of language? In ACL 2019-57th Annual Meeting of the Association for Computational Linguistics, 2019

Reference 27

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This paper cites Locating and editing factual associations in gpt.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Locating and editing factual associations in gpt

Reference 28

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This paper cites How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study

Reference 29

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This paper cites Deep residual learning for image recognition.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Deep residual learning for image recognition

Reference 30

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This paper cites Editing Large Language Models: Problems, Methods, and Opportunities.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Editing Large Language Models: Problems, Methods, and Opportunities

Reference 31

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This paper cites EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models

Reference 32

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This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment The Unreasonable Ineffectiveness of the Deeper Layers

Reference 33

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This paper cites Flexible group-level pruning of deep neural networks for on-device machine learning.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Flexible group-level pruning of deep neural networks for on-device machine learning

Reference 34

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Efficient joint optimization of layer-adaptive weight pruning in deep neural networks

Reference 35

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 36

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Mole: Mixture of lora experts

Reference 37

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs

Reference 38

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

Reference 39

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Stanford alpaca: An instruction-following llama model

Reference 40

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This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023

Reference 41

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This paper cites Language model evaluation harness (package version caaf9ab).

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Language model evaluation harness (package version caaf9ab)

Reference 42

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment BoolQ: Exploring the surprising difficulty of natural yes/no questions

Reference 43

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Piqa: Reasoning about physical commonsense in natural language

Reference 44

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This paper cites Hellaswag: Can a machine really finish your sentence? In ACL, 2019.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Hellaswag: Can a machine really finish your sentence? In ACL, 2019

Reference 45

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 46

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 47

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This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 48

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Measuring Massive Multitask Language Understanding

Reference 49

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Pointer sentinel mixture models

Reference 50

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This paper cites Aligning books and movies: Towards story-like visual explanations by watching movies and reading books.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Aligning books and movies: Towards story-like visual explanations by watching movies and reading books

Reference 51

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Attention is all you need

Reference 52

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 53

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Unresolved cited work

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This paper cites Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$

Reference 55

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This paper cites TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer

Reference 56

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Scaling Laws for Neural Language Models

Reference 57

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This paper cites Pythia: A suite for analyzing large language models across training and scaling.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Pythia: A suite for analyzing large language models across training and scaling

Reference 58

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Observation 14985555-59ac-44e1-8547-bda8b4f4c931 · outbound

This paper cites GLM: General Language Model Pretraining with Autoregressive Blank Infilling.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment GLM: General Language Model Pretraining with Autoregressive Blank Infilling

Reference 59

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This paper cites OPT: Open Pre-trained Transformer Language Models.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment OPT: Open Pre-trained Transformer Language Models

Reference 60

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This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 61

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Observation b33d280d-b0b7-4812-bf90-27c9eba21c60 · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Specializing smaller language models towards multi-step reasoning

Reference 62

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This paper cites Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Reference 63

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This paper cites Optq: Accurate quantization for generative pre-trained transformers.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Optq: Accurate quantization for generative pre-trained transformers

Reference 64

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment LLM.int8(): 8-bit matrix multiplication for transformers at scale

Reference 65

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Observation 7767e87b-1cd3-4e64-bcd6-8c7d2ebda0bd · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 66

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment GPTQ: Accurate post-training compression for generative pretrained transformers

Reference 67

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Spqr: A sparse-quantized representation for near-lossless llm weight compression, 2023

Reference 68

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 69

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 70

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Efficient memory management for large language model serving with pagedattention

Reference 71

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Llm-pruner: On the structural pruning of large language models, 2023

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:35:31.400730Z

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Observation e6c46938-4012-4947-b6b3-73e52a18c3dd · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch

Reference 73

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Slimmable Neural Networks

Reference 74

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Universally slimmable networks and improved training techniques

Reference 75

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Observation 306ff83c-6aae-470e-bf53-c0e8dc670e27 · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

Reference 76

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Adabits: Neural network quantization with adaptive bit-widths

Reference 77

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Observation 1de285e0-fa7b-4d95-ad15-87b8462be3c1 · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Switchable Precision Neural Networks

Reference 78

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Any-precision deep neural networks

Reference 79

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Observation 07fa6e38-2884-47e1-9a07-4cc8ab5e4d49 · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs

Reference 80

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Flextron: Many-in-One Flexible Large Language Model

Reference 81

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 82

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Limitations

Reference 83

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:35:31.303621Z

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the paper does not include experiments

Reference 85

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:35:31.272817Z

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment 5.1 of our paper and also provided sufficient references

Reference 87

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the paper does not include experiments

Reference 88

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment 5.1 of our paper

Reference 89

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 90

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 91

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the paper poses no such risks

Reference 92

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Observation 3c702b83-c509-48f1-a92a-b8fe33c1a627 · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the paper does not use existing assets

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:35:31.159213Z

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Observation c82ef08d-fa6a-4304-886c-de4e33e462af · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the paper does not release new assets

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:35:31.143558Z

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Observation 3729f352-9fba-40bb-89d5-9291098ffdaa · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:35:31.127838Z

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Observation 9c54f489-23f4-41e9-b7e4-311f76b1a11e · outbound

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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:35:31.110658Z

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Pith citing papers