README.md

---
license: mit
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
language:
- en
---

# DeepSeek-R1-Distill-Qwen-1.5B-awq-asym-uint4-g128-lmhead-onnx-hybrid
- ## Introduction
  This model was created by applying [Quark](https://quark.docs.amd.com/latest/index.html) with calibration samples from Pile dataset.
- ## Quantization Strategy
  - ***Quantized Layers***: All linear layers
  - ***Weight***: uint4 asymmetric per-group, group_size=128
- ## Quick Start
1. [Download and install Quark](https://quark.docs.amd.com/latest/install.html)
2. Run the quantization script in the example folder using the following command line:
    ```sh
    export MODEL_DIR = [local model checkpoint folder] or Qwen2.5-Math-1.5B
    # single GPU
    python quantize_quark.py --model_dir $MODEL_DIR \
                            --output_dir output_dir $MODEL_NAME-awq-asym-uint4-g128-lmhead \
                            --quant_scheme w_uint4_per_group_asym \
                            --num_calib_data 128 \
                            --quant_algo awq \
                            --dataset pileval_for_awq_benchmark \
                            --seq_len 512 \
                            --model_export hf_format \
                            --data_type bfloat16 \
                            --exclude_layers
    # cpu
    python quantize_quark.py --model_dir $MODEL_DIR \
                            --output_dir output_dir $MODEL_NAME-awq-asym-uint4-g128-lmhead \
                            --quant_scheme w_uint4_per_group_asym \
                            --num_calib_data 128 \
                            --quant_algo awq \
                            --dataset pileval_for_awq_benchmark \
                            --seq_len 512 \
                            --model_export hf_format \
                            --data_type bfloat16 \
                            --exclude_layers \
                            --device cpu
    ```

#### License
Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.

MIT License

Copyright (c) 2023 DeepSeek

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