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docs/quantization/wint2.md

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+# WINT2 Quantization
+
+Weights are compressed offline using the CCQ (Convolutional Coding Quantization) method. The actual stored numerical type of weights is INT8, with 4 weights packed into each INT8 value, equivalent to 2 bits per weight. Activations are not quantized. During inference, weights are dequantized and decoded in real-time to BF16 numerical type, and calculations are performed using BF16 numerical type.
+- **Supported Hardware**: GPU
+- **Supported Architecture**: MoE architecture
+
+CCQ WINT2 is generally used in resource-constrained and low-threshold scenarios. Taking ERNIE-4.5-300B-A47B as an example, weights are compressed to 89GB, supporting single-card deployment on 141GB H20.
+
+## Run WINT2 Inference Service
+
+```
+python -m fastdeploy.entrypoints.openai.api_server \
+       --model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle \
+       --port 8180 --engine-worker-queue-port 8181 \
+       --cache-queue-port 8182 --metrics-port 8182 \
+       --tensor-parallel-size 1 \
+       --max-model-len 32768 \
+       --max-num-seqs 32
+```
+
+By specifying `--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle`, the offline quantized WINT2 model can be automatically downloaded from AIStudio. In the config.json file of this model, there will be WINT2 quantization-related configuration information, so there's no need to set `--quantization` when starting the inference service.
+
+Example of quantization configuration in the model's config.json file:
+
+```
+"quantization_config": {
+    "dense_quant_type": "wint8",
+    "moe_quant_type": "w4w2",
+    "quantization": "wint2",
+    "moe_quant_config": {
+    "moe_w4_quant_config": {
+        "quant_type": "wint4",
+        "quant_granularity": "per_channel",
+        "quant_start_layer": 0,
+        "quant_end_layer": 6
+    },
+    "moe_w2_quant_config": {
+        "quant_type": "wint2",
+        "quant_granularity": "pp_acc",
+        "quant_group_size": 64,
+        "quant_start_layer": 7,
+        "quant_end_layer": 53
+    }
+  }
+}
+```
+
+- For more deployment tutorials, please refer to [get_started](../get_started/ernie-4.5.md);
+- For more model descriptions, please refer to [Supported Model List](https://console.cloud.baidu-int.com/devops/icode/repos/baidu/paddle_internal/FastDeploy/blob/feature%2Finference-refactor-20250528/docs/supported_models.md).
+
+## WINT2 Performance
+
+On the ERNIE-4.5-300B-A47B model, comparison of WINT2 vs WINT4 performance:
+
+| Test Set | Dataset Size | WINT4 | WINT2 |
+|---------|---------|---------|---------|
+| IFEval |500|88.17 | 85.40 |
+|BBH|6511|94.43|92.02|
+|DROP|9536|91.17|89.97|