Pre-trained models (LNQ + GQuant)
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Model Card
- Base model:
meta-llama/Llama-2-70b-hf - Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 3
- 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
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Base model
meta-llama/Llama-2-70b-hf