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amd/Llama-3.2-3B-Instruct-FP8-KV
Llama-3.2-3B-Instruct-FP8-KV is a machine learning model from amd. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.2.
- Introduction This model was created by applying Quark with calibration samples from Pile dataset. - Quantization Stragegy - Quantized Layers: All linear layers excluding "lmhead" - Weight: FP8 symmetric per-tensor -…
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.safetensors3.6 GB · 100%
How the weights are stored.
F8_E4M32.8B · 88%
From the Hugging Face model README
export MODEL_DIR = [local model checkpoint folder] or meta-llama/Llama-3.2-3B-Instruct
# single GPU
python3 quantize_quark.py \
--model_dir $MODEL_DIR \
--output_dir Llama-3.2-3B-Instruct-FP8-KV \
--quant_scheme w_fp8_a_fp8 \
--kv_cache_dtype fp8 \
--num_calib_data 128 \
--model_export quark_safetensors \
--no_weight_matrix_merge \
--custom_mode fp8
# If model size is too large for single GPU, please use multi GPU instead.
python3 quantize_quark.py \
--model_dir $MODEL_DIR \
--output_dir Llama-3.2-3B-Instruct-FP8-KV \
--quant_scheme w_fp8_a_fp8 \
--kv_cache_dtype fp8 \
--num_calib_data 128 \
--model_export quark_safetensors \
--no_weight_matrix_merge \
--multi_gpu \
--custom_mode fp8
Quark has its own export format and allows FP8 quantized models to be efficiently deployed using the vLLM backend(vLLM-compatible).
Quark currently uses perplexity(PPL) as the evaluation metric for accuracy loss before and after quantization.The specific PPL algorithm can be referenced in the quantize_quark.py. The quantization evaluation results are conducted in pseudo-quantization mode, which may slightly differ from the actual quantized inference accuracy. These results are provided for reference only.
Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.