README.md

---
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
- **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).


**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 = "unsloth/DeepSeek-R1-0528-BF16"
exclude_layers = ["lm_head","*mlp.gate"]
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)
```

### Accuracy

<table>
  <tr>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong>DeepSeek-R1-0528</strong>
   </td>
   <td><strong>DeepSeek-R1-0528-ptpc(this model)</strong>
   </td>
  </tr>
  <tr>
   <td>GPQA-diamond
   </td>
   <td>80.72
   </td>
   <td>80.05
   </td>
  </tr>
</table>

### Reproduction

Docker: rocm/vllm-private:rocm7.1_ubuntu22.04_vllm0.11.2_ptpc_fp8

vllm version: 0.10.1.1+rocm710

aiter version: 0.1.6.post2.dev55+g59bd8ff2c

lighteval version: 0.12.2
```
MODEL_ARGS="model_name=/amd/DeepSeek-R1-0528-ptpc,dtype=auto,tensor_parallel_size=8,max_model_length=71536,max_num_batched_tokens=65536,gpu_memory_utilization=0.9,generation_parameters={max_new_tokens:65536,temperature:0.6,top_p:0.95,seed:100}"
lighteval vllm $MODEL_ARGS "lighteval|gpqa:diamond|0"
```

# 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.