DeepSeek-V3 Weight File Documentation

New Fields in config.json

• model_type: Specifies the model type, which is updated to deepseek_v3 in this release.
• num_nextn_predict_layers: Indicates the number of Multi-Token Prediction (MTP) Modules. The open-sourced V3 weights include 1 MTP Module .
• quantization_config: Describes the configuration for FP8 quantization.

Weight Structure Overview

The DeepSeek-V3 weight file consists of two main components: Main Model Weights and MTP Modules.

1. Main Model Weights

• Composition:
• Input/output embedding layers and a complete set of 61 Transformer hidden layers.
• Parameter Count:
• Total parameters: 671B
• Activation parameters: 36.7B (including 0.9B for Embedding and 0.9B for the output Head).

Structural Details

• Embedding Layer:
• model.embed_tokens.weight
• Transformer Hidden Layers:
• model.layers.0 to model.layers.60, totaling num_hidden_layers layers.
• Output Layer:
• model.norm.weight
• lm_head.weight

2. Multi-Token Prediction (MTP) Modules

• Composition:
• Additional MTP Modules defined by the num_nextn_predict_layers field. In this model, the value is set to 1.
• Parameter Count:
• Parameters: 11.5B unique parameters, excluding the shared 0.9B Embedding and 0.9B output Head).
• Activation parameters: 2.4B (including the shared 0.9B Embedding and 0.9B output Head).

Structural Details

• embed_tokens: Shares parameters with the Embedding layer of the Main Model weights.
• enorm & hnorm: RMSNorm parameters required for speculative decoding.
• eh_proj: Parameters for dimensionality reduction projection on the norm results.
• Additional Transformer Hidden Layer:
• model.layers.61.self_attn & mlp (structure identical to the Main Model hidden layers).
• shared_head: Shares parameters with the output Head of the Main Model weights.

Loading Rules

• Main Model Weights: Loaded via the num_hidden_layers parameter in config.json.
• MTP Modules: Loaded via the num_nextn_predict_layers parameter, with layer IDs appended immediately after the Main Model hidden layers. For example:
• If num_hidden_layers = 61 and num_nextn_predict_layers = 1, the MTP Module's layer ID is 61.

FP8 Weight Documentation

DeepSeek-V3 natively supports FP8 weight format with 128x128 block scaling.

FP8 Configuration

The FP8 weight file introduces a quantization_config field to describe the quantization method. Below is an example configuration:

"quantization_config": {
  "activation_scheme": "dynamic",
  "fmt": "e4m3",
  "quant_method": "fp8",
  "weight_block_size": [128, 128]
}

• Quantization Format:
• Format type: fp8 and e4m3 (corresponding to torch.float8_e4m3fn).
• Weight block size: 128x128.
• Activation Quantization Scheme:
• Utilizes dynamic activation quantization (dynamic).

Dequantization Method

The FP8 weight file includes a weight_scale_inv field, which stores the dequantization scale for each weight block.

• Storage Format: float32 Tensor, stored alongside the weight data.
• Dequantization Formula:
• If the weight block is not aligned to 128, it is zero-padded to 128 before calculating the scale. After quantization, the padded portion is removed.
• The dequantization process is performed as: (128x128 weight block) * weight_scale_inv.

Through dequantization of the FP8 weights, runtime operations enable online quantization at a granularity of per-token-per-128-channel.