Model Card

• Base model: meta-llama/Meta-Llama-3-70B
• 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): 1

How to run

• Follow the instruction in https://github.com/snu-mllab/GuidedQuant.

References

• Model Paper

Base model