metadata
base_model:
- unsloth/DeepSeek-R1-0528-BF16
language:
- en
library_name: transformers
license: mit
Model Overview
- Model Architecture: DeepSeek-R1-0528
- Input: Text
- Output: Text
- Supported Hardware Microarchitecture: AMD MI350/MI355
- ROCm: 7.0
- PyTorch: 2.8.0
- Transformers: 4.56.1
- Operating System(s): Linux
- Inference Engine: SGLang/vLLM
Model Details
This model is adapted from unsloth/DeepSeek-R1-0528-BF16 by adding an MTP layer (specifically, the 61st layer) in the modeling_deepseek.py file. With this modification, it is possible to use AMD-Quark to quantize the DeepSeek-R1-0528 model with the MTP layer included.
Important Notes:
- When loading this model, you must set
trust_remote_code=Trueto ensure that changes related to the MTP layer inmodeling_deepseek.pytake effect. - After loading this model with
transformers, evaluation should NOT be performed directly. The reason is that the forward function for the added MTP layer inmodeling_deepseek.pyis implemented only for calibration during the quantization process, so computation is not guaranteed to be the same as the original DeepSeek-R1-0528. - Therefore, when quantizing with AMD-Quark, you must add the
--skip_evaluationoption to skip the evaluation step and only perform quantization.
For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
Below is an example of how to quantize this model:
cd Quark/examples/torch/language_modeling/llm_ptq/
exclude_layers="lm_head *self_attn* *mlp.gate *eh_proj"
python3 quantize_quark.py --model_dir $MODEL_DIR \
--quant_scheme w_mxfp4_a_mxfp4 \
--num_calib_data 32 \
--output_dir $output_dir \
--exclude_layers $exclude_layers \
--dataset pileval \
--multi_gpu \
--model_export hf_format \
--trust_remote_code \
--skip_evaluation \
--seq_len 512
For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
License
Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.