meta-llama/Llama-3.2-1B-Instruct
Introduction
- Quantization Tool: Quark 0.6.0
- OGA Model Builder: v0.5.1
- Postprocess
Quantization Strategy
- AWQ / Group 128 / Asymmetric / UINT4 Weights / FP16 activations
- Excluded Layers: None
python3 quantize_quark.py \ --model_dir "$model" \ --output_dir "$output_dir" \ --quant_scheme w_uint4_per_group_asym \ --num_calib_data 128 \ --quant_algo awq \ --dataset pileval_for_awq_benchmark \ --seq_len 512 \ --model_export quark_safetensors \ --data_type float16 \ --exclude_layers [] \ --custom_mode awq
OGA Model Builder
python builder.py \ -i <quantized safetensor model dir> \ -o <oga model output dir> \ -p int4 \ -e dml
- PostProcessed to generate Hybrid Model
Quick Start
For quickstart, refer to hybrid-llm-artifacts_1.3.0.zip available in RyzenAI-SW-EA
Evaluation scores
The perplexity measurement is run on the wikitext-2-raw-v1 (raw data) dataset provided by Hugging Face. Perplexity score measured for prompt length 2k is 15.3878.
License
Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.
MIT License
Copyright (c) 2024 Advanced Micro Devices, Inc
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Model tree for amd/Llama-3.2-1B-Instruct-awq-g128-int4-asym-fp16-onnx-hybrid
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meta-llama/Llama-3.2-1B-Instruct