chore: import upstream snapshot with attribution

docs/supported_models/text_generation/diffusion_language_models.md

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+# Diffusion Language Models
+
+Diffusion language models have shown promise for non-autoregressive text generation with parallel decoding capabilities. Unlike auto-regressive language models, different diffusion language models require different decoding strategies.
+
+## Example Launch Command
+
+SGLang supports different DLLM algorithms such as `LowConfidence` and `JointThreshold`.
+
+```shell
+python3 -m sglang.launch_server \
+  --model-path inclusionAI/LLaDA2.0-mini \ # example HF/local path
+  --dllm-algorithm LowConfidence \
+  --dllm-algorithm-config ./config.yaml \ # Optional. Uses the algorithm's default if not set.
+  --host 0.0.0.0 \
+  --port 30000
+```
+
+## Example Configuration File
+
+Depending on the algorithm selected, the configuration parameters vary.
+
+LowConfidence Config:
+
+```yaml
+# Confidence threshold for accepting predicted tokens
+# - Higher values: More conservative, better quality but slower
+# - Lower values: More aggressive, faster but potentially lower quality
+# Range: 0.0 - 1.0
+threshold: 0.95
+
+# Default: 32, for LLaDA2MoeModelLM
+block_size: 32
+```
+
+JointThreshold Config:
+
+```yaml
+# Decoding threshold for Mask-to-Token (M2T) phase
+# - Higher values: More conservative, better quality but slower
+# - Lower values: More aggressive, faster but potentially lower quality
+# Range: 0.0 - 1.0
+threshold: 0.5
+# Decoding threshold for Token-to-Token (T2T) phase
+# Range: 0.0 - 1.0
+# Setting to 0.0 allows full editing (recommended for most cases).
+edit_threshold: 0.0
+# Max extra T2T steps after all masks are removed. Prevents infinite loops.
+max_post_edit_steps: 16
+# 2-gram repetition penalty (default 0).
+# An empirical value of 3 is often sufficient to mitigate most repetitions.
+penalty_lambda: 0
+```
+
+## Example Client Code Snippet
+
+Just like other supported models, diffusion language models can be used via the REST API or Python client.
+
+Python client example for making a generation request to the launched server:
+
+```python
+import sglang as sgl
+
+def main():
+    llm = sgl.Engine(model_path="inclusionAI/LLaDA2.0-mini",
+                     dllm_algorithm="LowConfidence",
+                     max_running_requests=1,
+                     trust_remote_code=True)
+
+    prompts = [
+        "<role>SYSTEM</role>detailed thinking off<|role_end|><role>HUMAN</role> Write a brief introduction of the great wall <|role_end|><role>ASSISTANT</role>"
+    ]
+
+    sampling_params = {
+        "temperature": 0,
+        "max_new_tokens": 1024,
+    }
+
+    outputs = llm.generate(prompts, sampling_params)
+    print(outputs)
+
+if __name__ == '__main__':
+    main()
+```
+
+Curl example for making a generation request to the launched server:
+
+```bash
+curl -X POST "http://127.0.0.1:30000/generate" \
+     -H "Content-Type: application/json" \
+     -d '{
+        "text": [
+            "<role>SYSTEM</role>detailed thinking off<|role_end|><role>HUMAN</role> Write the number from 1 to 128 <|role_end|><role>ASSISTANT</role>",
+            "<role>SYSTEM</role>detailed thinking off<|role_end|><role>HUMAN</role> Write a brief introduction of the great wall <|role_end|><role>ASSISTANT</role>"
+        ],
+        "stream": true,
+        "sampling_params": {
+            "temperature": 0,
+            "max_new_tokens": 1024
+        }
+    }'
+```
+
+## Supported Models
+
+Below the supported models are summarized in a table.
+
+| Model Family               | Example Model                | Description                                                                                          |
+| -------------------------- | ---------------------------- | ---------------------------------------------------------------------------------------------------- |
+| **LLaDA2.0 (mini, flash)** | `inclusionAI/LLaDA2.0-flash` | LLaDA2.0-flash is a diffusion language model featuring a 100B Mixture-of-Experts (MoE) architecture. |
+| **SDAR (JetLM)**           | `JetLM/SDAR-8B-Chat`         | SDAR series diffusion language model (Chat), dense architecture.                                 |
+| **SDAR (JetLM)**           | `JetLM/SDAR-30B-A3B-Chat`    | SDAR series diffusion language model (Chat), MoE architecture.                                   |