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Collections including paper arxiv:2306.00978
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Jul 1
Jul 1-
Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models
Paper • 2504.04823 • Published • 31 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 10 -
The case for 4-bit precision: k-bit Inference Scaling Laws
Paper • 2212.09720 • Published • 3
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Aug 27, 2024
Aug 27, 2024-
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
Paper • 2407.11062 • Published • 10 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 10
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Mar 20, 2024
Mar 20, 2024Collection by
May 31, 2024
May 31, 2024-
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
Paper • 2307.13304 • Published • 2 -
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Paper • 2306.03078 • Published • 3 -
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
Paper • 2308.13137 • Published • 18 -
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 10
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11 days ago
11 days ago-
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Paper • 2208.07339 • Published • 5 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Paper • 2211.10438 • Published • 6 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 55
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Nov 26, 2024
Nov 26, 2024
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Jun 4, 2024
Jun 4, 2024-
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 10 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 624
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Jan 15, 2024
Jan 15, 2024-
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Paper • 2312.15166 • Published • 59 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 244 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 46 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 55
Collection by
May 27
May 27-
FP8-LM: Training FP8 Large Language Models
Paper • 2310.18313 • Published • 33 -
LLM-FP4: 4-Bit Floating-Point Quantized Transformers
Paper • 2310.16836 • Published • 14 -
TEQ: Trainable Equivalent Transformation for Quantization of LLMs
Paper • 2310.10944 • Published • 10 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1
Collection by
4 days ago
4 days agoCollection by
11 days ago
11 days ago-
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Paper • 2208.07339 • Published • 5 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Paper • 2211.10438 • Published • 6 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 55
Collection by
Jul 1
Jul 1-
Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models
Paper • 2504.04823 • Published • 31 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 10 -
The case for 4-bit precision: k-bit Inference Scaling Laws
Paper • 2212.09720 • Published • 3
Collection by
Nov 26, 2024
Nov 26, 2024Collection by
Aug 27, 2024
Aug 27, 2024-
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
Paper • 2407.11062 • Published • 10 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 10
Collection by
Jun 4, 2024
Jun 4, 2024-
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 10 -
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Paper • 2210.17323 • Published • 8 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 624
Collection by
Mar 20, 2024
Mar 20, 2024Collection by
Jan 15, 2024
Jan 15, 2024-
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Paper • 2312.15166 • Published • 59 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 244 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 46 -
QLoRA: Efficient Finetuning of Quantized LLMs
Paper • 2305.14314 • Published • 55
Collection by
May 31, 2024
May 31, 2024-
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
Paper • 2307.13304 • Published • 2 -
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Paper • 2306.03078 • Published • 3 -
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
Paper • 2308.13137 • Published • 18 -
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper • 2306.00978 • Published • 10
Collection by
May 27
May 27-
FP8-LM: Training FP8 Large Language Models
Paper • 2310.18313 • Published • 33 -
LLM-FP4: 4-Bit Floating-Point Quantized Transformers
Paper • 2310.16836 • Published • 14 -
TEQ: Trainable Equivalent Transformation for Quantization of LLMs
Paper • 2310.10944 • Published • 10 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1