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Update README_WEIGHTS.md

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# DeepSeek-V3 Weight File Documentation
## New Fields in `config.json`
## New Fields in [`config.json`](https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/config.json)
- **model_type**: Specifies the model type, which is updated to `deepseek_v3` in this release.
- **model_type**: Specifies the model type, set 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.
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### 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.
- Additional MTP Modules defined by the `num_nextn_predict_layers` field. For this model, it 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).
@ -75,7 +75,7 @@ The FP8 weight file introduces a `quantization_config` field to describe the qua
```
- **Quantization Format**:
- Format type: `fp8` and `e4m3` (corresponding to `torch.float8_e4m3fn`).
- Format type: `fp8` and `e4m3` (corresponds to `torch.float8_e4m3fn`).
- Weight block size: `128x128`.
- **Activation Quantization Scheme**:
- Utilizes dynamic activation quantization (`dynamic`).
@ -86,8 +86,8 @@ The FP8 weight file includes a `weight_scale_inv` field, which stores the dequan
- **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`.
- If a weight block is not aligned to 128, it is zero-padded to 128 before the scale is calculated. After quantization, the padded portion is removed.
- Dequantization 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`.