| # 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: | |
| ```json | |
| "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`. | |
| --- | |