Clone Conda Environment
作者：XD / 发表： 2024年2月27日 21:54 / 编程笔记/ 阅读量：2164
Clone Conda Environments
Clone Conda Environments
Setting Up GPT4 API Without a Proxy
Merge Safetensors to Bin File
Check the Index and Token from Tiktoken
Lucid Plugin from ChatGPT to Creating the Diagram
Use md5sum to Verify File Integrity
llama.cpp: Definitions of Q2_K, Q3_K, Q4_K, Q5_K, Q6_K, and Q8_K Structures
llama.cpp: Efficient 6-bit Data Packing in an 8-bit Array
Setting Up v2rayNG with Tencent Cloud Silicon Valley Lighthouse 利用腾讯云配置自己的v2ray
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Paper: https://arxiv.org/abs/2211.10438
Code: https://github.com/mit-han-lab/smoothquant
Organization: MIT
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper: https://arxiv.org/abs/2306.00978
Code: https://github.com/mit-han-lab/llm-awq/
Organization: MIT
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats
Paper: https://arxiv.org/abs/2307.09782
Code: https://github.com/microsoft/DeepSpeed
Organization: Microsoft
QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models
Paper: https://arxiv.org/abs/2310.09259
Code: https://github.com/IST-DASLab/QUIK
Organization: ETH Zurich
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Paper: https://arxiv.org/abs/2306.03078
Code: https://github.com/Vahe1994/SpQR
Organization: University of Washington
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of Large Language Models
Paper: https://arxiv.org/abs/2309.05516
Code: https://github.com/intel/neural-compressor
Organization: Intel
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Paper: https://arxiv.org/abs/2309.02784
Code: None
Organization: Meituan
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