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layerwise-Meta-Llama-3-8B-w4-redpajama_s1024_blk4096_g1_iter3_cd4 – AI Model by jusjinuk | AlphaNeural AI
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layerwise-Meta-Llama-3-8B-w4-redpajama_s1024_blk4096_g1_iter3_cd4
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llama
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Base model:
meta-llama/Meta-Llama-3-8B
Quantization method: LNQ with GuidedQuant Hessian
Target bit-width: 4
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): 1
How to run
Follow the instruction in
https://github.com/snu-mllab/GuidedQuant
.
References
Model Paper
