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README_WEIGHTS.md

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+# 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`.
+
+---