Create README.md

README.md

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+---
+license: mit
+base_model:
+- deepseek-ai/DeepSeek-R1-0528
+---
+
+**Note that the MTP layers of this model are also PTPC-quantized.**
+
+# Model Overview
+
+- **Model Architecture:** DeepSeek-R1-0528
+  - **Input:** Text
+  - **Output:** Text
+- **Supported Hardware Microarchitecture:** AMD MI350/MI355
+- **ROCm**: 7.0
+- **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:** Perchannel, FP8E4M3, Static
+  - **Activation quantization:** Pertoken, FP8E4M3, 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 FP8E4M3 PTPC 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). The weights are quantized to FP8 and activations are quantized to FP8.
+
+**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).
+You need to manually modify the transformers so that it can load the MTP layer. You can also directly use our modified model [amd/DeepSeek-R1-0528-BF16](https://huggingface.co/amd/DeepSeek-R1-0528-BF16) to perform quantitative analysis.
+
+**Quantization scripts:**
+```
+# pip install amd-quark
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from quark.torch import ModelQuantizer, export_safetensors
+from quark.torch.quantization import FP8E4M3PerChannelSpec
+from quark.torch.quantization.config.config import Config, QuantizationConfig
+
+ckpt_path = "amd/DeepSeek-R1-0528-BF16"
+exclude_layers = ["lm_head","*mlp.gate", "model.layers.61.eh_proj", "model.layers.61.shared_head.head"]
+output_dir = ckpt_path.rstrip("/").split("/")[-1] + "-ptpc"
+
+# Load the original floating-point model
+model = AutoModelForCausalLM.from_pretrained(ckpt_path, device_map="auto", torch_dtype="auto", trust_remote_code=True)
+model.eval()
+tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
+
+# Set the quantization configuration
+FP8_PER_CHANNEL_SPEC = FP8E4M3PerChannelSpec(is_dynamic=False, ch_axis=0).to_quantization_spec()
+FP8_PER_TOKEN_DYNAMIC_SPEC = FP8E4M3PerChannelSpec(is_dynamic=True, ch_axis=1).to_quantization_spec()
+W_FP8_PER_CHANNEL_STATIC_A_FP8_PER_TOKEN_DYNAMIC_CONFIG = QuantizationConfig(input_tensors=FP8_PER_TOKEN_DYNAMIC_SPEC, weight=FP8_PER_CHANNEL_SPEC)
+quant_config = Config(global_quant_config=W_FP8_PER_CHANNEL_STATIC_A_FP8_PER_TOKEN_DYNAMIC_CONFIG, exclude=exclude_layers)
+
+# Apply quantization
+quantizer = ModelQuantizer(quant_config)
+model = quantizer.quantize_model(model)
+
+# Export quantized model
+model = quantizer.freeze(model)
+export_safetensors(model, output_dir)
+tokenizer.save_pretrained(output_dir)
+```
+
+# Deployment
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backends.
+
+# License
+Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.