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layerwise-Llama-2-70b-hf-w2-redpajama_s1024_blk4096_g2_iter2_cd4 – AI Model by jusjinuk | AlphaNeural AI
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layerwise-Llama-2-70b-hf-w2-redpajama_s1024_blk4096_g2_iter2_cd4
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pytorch
llama
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Base model:
meta-llama/Llama-2-70b-hf
Quantization method: LNQ with GuidedQuant Hessian
Target bit-width: 2
Backend kernel: Any-Precision-LLM kernel (
ap-gemv
)
Calibration data: RedPajama (1024 sentences / 4096 tokens)
Calibration objective: Next-token prediction
num_groups (for GuidedQuant Hessian): 2
How to run
Follow the instruction in
https://github.com/snu-mllab/GuidedQuant
.
References
Model Paper
