create model

README.md

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----
-license: mit
-base_model:
-- deepseek-ai/DeepSeek-R1-0528
----
-
-
-# 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**: 4.53.0
-- **Operating System(s):** Linux
-- **Inference Engine:** [SGLang](https://docs.sglang.ai/)/[vLLM](https://docs.vllm.ai/en/latest/)
-- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html) (V0.10)
-  - **Weight quantization:** OCP MXFP4, Static
-  - **Activation quantization:** OCP MXFP4, Dynamic
-- **Calibration Dataset:** [Pile](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)
-
-This model was built with deepseek-ai DeepSeek-R1-0528 model by applying [AMD-Quark](https://quark.docs.amd.com/latest/index.html) for MXFP4 quantization.
-
-# Model Quantization
-
-The model was quantized from [deepseek-ai/DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). Both weights and activations were quantized to MXFP4 format. 
-
-**Preprocessing requirement:**
-
-Before executing the quantization script below, the original FP8 model must first be dequantized to BFloat16.
-You can either perform the dequantization manually using this [conversion script](https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/fp8_cast_bf16.py), or use the pre-converted BFloat16 model available at [unsloth/DeepSeek-R1-0528-BF16](https://huggingface.co/unsloth/DeepSeek-R1-0528-BF16).
-
-**Quantization scripts:**
-```
-cd Quark/examples/torch/language_modeling/llm_ptq/
-exclude_layers="*self_attn* *mlp.gate.* *lm_head"
-python3 quantize_quark.py --model_dir $MODEL_DIR \
-                          --quant_scheme w_mxfp4_a_mxfp4 \
-                          --group_size 32 \
-                          --num_calib_data 128 \
-                          --exclude_layers $exclude_layers \
-                          --skip_evaluation \
-                          --multi_gpu \
-                          --model_export hf_format \
-                          --output_dir amd/DeepSeek-R1-0528-MXFP4-Preview
-```
-
-# Deployment
-
-This model can be deployed efficiently using the [SGLang](https://docs.sglang.ai/) and [vLLM](https://docs.vllm.ai/en/latest/) backends.
-
-## Evaluation
-
-The model was evaluated on AIME24, GPQA Diamond, and MATH-500 benchmarks using the [lighteval](https://github.com/huggingface/lighteval/tree/v0.10.0) framework. Each benchmark was run 10 times with different random seeds for reliable performance estimation.
-
-### Accuracy
-
-<table>
-  <tr>
-   <td><strong>Benchmark</strong>
-   </td>
-   <td><strong>DeepSeek-R1-0528 </strong>
-   </td>
-   <td><strong>DeepSeek-R1-0528-MXFP4-Preview(this model)</strong>
-   </td>
-   <td><strong>Recovery</strong>
-   </td>
-  </tr>
-  <tr>
-   <td>AIME24 
-   </td>
-   <td>88.00
-   </td>
-   <td>85.00
-   </td>
-   <td>96.59%
-   </td>
-  </tr>
-  <tr>
-   <td>GPQA Diamond 
-   </td>
-   <td>79.90
-   </td>
-   <td>79.34
-   </td>
-   <td>99.31%
-   </td>
-  </tr>
-  <tr>
-   <td>MATH-500 
-   </td>
-   <td>97.06
-   </td>
-   <td>97.84
-   </td>
-   <td>100.80%
-   </td>
-  </tr>
-</table>
-
-
-### Reproduction
-
-The results of AIME24, MATH-500, and GPQA Diamond, were obtained using forked [lighteval](https://github.com/zhaolin-amd/lighteval/tree/v0.10-release-custom) and vLLM docker (emulation qdq) `rocm/vllm-private:pytorch-vllm-gfx950-mxfp4-mxfp6-v3`.
-
-```
-# Set docker env
-export VLLM_QUARK_F4F6_OFFLINE_DEQUANT_TMPENVVAR=1
-
-# Set model args
-OUTPUT_DIR="results/DeepSeek-R1-0528-MXFP4-Preview-Seed"
-LOG="logs/deepseek_0528_maxfp4.log"
-
-# Evaluating 10 rounds 
-for i in $(seq 1 10); do
-    # seed in [0, 2**30 - 1]
-    SEED=$(shuf -i 0-1073741823 -n 1)
-    MODEL_ARGS="model_name=amd/DeepSeek-R1-0528-MXFP4-Preview,dtype=bfloat16,tensor_parallel_size=8,max_model_length=71536,max_num_batched_tokens=32768,gpu_memory_utilization=0.85,generation_parameters={max_new_tokens:65536,temperature:0.6,top_p:0.95,seed:$SEED}"
-
-    lighteval vllm $MODEL_ARGS "custom|aime24_single|0|0,custom|math_500_single|0|0,custom|gpqa:diamond_single|0|0" \
-        --use-chat-template \
-        --output-dir "$OUTPUT_DIR/seed_$SEED" \
-        2>&1 | tee -a "$LOG"
-```
-
-# License
-Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.