[AMD] [Qwen 3.5 Day 0] Add Qwen 3.5 nightly accuracy tests (#19479)

docs/basic_usage/popular_model_usage.rst

@@ -13,6 +13,7 @@ For more usage examples and recipes, visit the `SGLang Cookbook <https://cookboo
    gpt_oss.md
    minimax_m2.md
    qwen3.md
+   qwen3_5.md
    qwen3_vl.md
    deepseek_ocr.md
    llama4.md

docs/basic_usage/qwen3_5.md

@@ -0,0 +1,76 @@
+# Qwen 3.5 Usage
+
+Qwen 3.5 is Alibaba's latest generation LLM featuring a hybrid attention architecture, advanced MoE with shared experts, and native multimodal capabilities.
+
+Key architecture features:
+- **Hybrid Attention**: Gated Delta Networks (linear, O(n) complexity) combined with full attention every 4th layer for high associative recall
+- **MoE with Shared Experts**: Top-8 active out of 64 routed experts plus a dedicated shared expert for universal features
+- **Multimodal**: DeepStack Vision Transformer with Conv3d for native image and video understanding
+
+## Launch Qwen 3.5 with SGLang
+
+### Dense Model
+
+To serve `Qwen/Qwen3.5-397B-A17B` on 8 GPUs:
+
+```bash
+python3 -m sglang.launch_server \
+    --model-path Qwen/Qwen3.5-397B-A17B \
+    --tp 8 \
+    --trust-remote-code
+```
+
+### AMD GPU (MI300X / MI325X / MI35X)
+
+On AMD Instinct GPUs, use the `triton` attention backend. Both the full attention layers and the Gated Delta Net (linear attention) layers use Triton-based kernels on ROCm:
+
+```bash
+SGLANG_USE_AITER=1 python3 -m sglang.launch_server \
+    --model-path Qwen/Qwen3.5-397B-A17B \
+    --tp 8 \
+    --attention-backend triton \
+    --trust-remote-code
+```
+
+```{tip}
+Set `SGLANG_USE_AITER=1` to enable AMD's optimized aiter kernels for MoE and GEMM operations.
+```
+
+### Configuration Tips
+
+- `--attention-backend`: Use `triton` on AMD GPUs for Qwen 3.5. The hybrid attention architecture (Gated Delta Networks + full attention) works best with the Triton backend on ROCm. The linear attention (GDN) layers always use Triton kernels internally via the `GDNAttnBackend`.
+- `--watchdog-timeout`: Increase to `1200` or higher for this large model, as weight loading takes significant time.
+- `--model-loader-extra-config '{"enable_multithread_load": true}'`: Enables parallel weight loading for faster startup.
+
+### Reasoning and Tool Calling
+
+Qwen 3.5 supports reasoning and tool calling via the Qwen3 parsers:
+
+```bash
+python3 -m sglang.launch_server \
+    --model-path Qwen/Qwen3.5-397B-A17B \
+    --tp 8 \
+    --trust-remote-code \
+    --reasoning-parser qwen3 \
+    --tool-call-parser qwen3_coder
+```
+
+## Accuracy Evaluation
+
+You can evaluate the model accuracy using `lm-eval`:
+
+```bash
+pip install lm-eval[api]
+
+lm_eval --model local-completions \
+    --model_args '{"base_url": "http://localhost:8000/v1/completions", "model": "Qwen/Qwen3.5-397B-A17B", "num_concurrent": 256, "max_retries": 10, "max_gen_toks": 2048}' \
+    --tasks gsm8k \
+    --batch_size auto \
+    --num_fewshot 5 \
+    --trust_remote_code
+```
+
+## Additional Resources
+
+- [AMD Day 0 Support for Qwen 3.5 on AMD Instinct GPUs](https://www.amd.com/en/developer/resources/technical-articles/2026/day-0-support-for-qwen-3-5-on-amd-instinct-gpus.html)
+- [HuggingFace Model Card](https://huggingface.co/Qwen/Qwen3.5-397B-A17B)

docs/supported_models/text_generation/generative_models.md

@@ -29,7 +29,7 @@ in the GitHub search bar.
 | **Kimi K2** (Thinking, Instruct)    | `moonshotai/Kimi-K2-Instruct`                    | Moonshot AI's 1 trillion parameter MoE model (32B active) with 128K–256K context; state-of-the-art agentic intelligence with stable long-horizon agency across 200–300 sequential tool calls. Features MLA attention and native INT4 quantization. [See Reasoning Parser docs](../advanced_features/separate_reasoning.ipynb)|
 | **Kimi Linear** (48B-A3B)           | `moonshotai/Kimi-Linear-48B-A3B-Instruct`        | Moonshot AI's hybrid linear attention model (48B total, 3B active) with 1M token context; features Kimi Delta Attention (KDA) for up to 6× faster decoding and 75% KV cache reduction vs full attention. |
 | **GPT-OSS**       | `openai/gpt-oss-20b`, `openai/gpt-oss-120b`       | OpenAI’s latest GPT-OSS series for complex reasoning, agentic tasks, and versatile developer use cases.|
-| **Qwen** (3, 3MoE, 3Next, 2.5, 2 series)       | `Qwen/Qwen3-0.6B`, `Qwen/Qwen3-30B-A3B` `Qwen/Qwen3-Next-80B-A3B-Instruct `      | Alibaba’s latest Qwen3 series for complex reasoning, language understanding, and generation tasks; Support for MoE variants along with previous generation 2.5, 2, etc. [SGLang provides Qwen3 specific reasoning parser](../advanced_features/separate_reasoning.ipynb)|
+| **Qwen** (3.5, 3, 3MoE, 3Next, 2.5, 2 series)       | `Qwen/Qwen3.5-397B-A17B`, `Qwen/Qwen3-0.6B`, `Qwen/Qwen3-30B-A3B`      | Alibaba’s latest Qwen3 series for complex reasoning, language understanding, and generation tasks; Support for MoE variants along with previous generation 2.5, 2, etc. [SGLang provides Qwen3 specific reasoning parser](../advanced_features/separate_reasoning.ipynb)|
 | **Llama** (2, 3.x, 4 series)        | `meta-llama/Llama-4-Scout-17B-16E-Instruct`       | Meta's open LLM series, spanning 7B to 400B parameters (Llama 2, 3, and new Llama 4) with well-recognized performance. [SGLang provides Llama-4 model-specific optimizations](../basic_usage/llama4.md)  |
 | **Mistral** (Mixtral, NeMo, Small3) | `mistralai/Mistral-7B-Instruct-v0.2`             | Open 7B LLM by Mistral AI with strong performance; extended into MoE (“Mixtral”) and NeMo Megatron variants for larger scale. |
 | **Gemma** (v1, v2, v3)              | `google/gemma-3-1b-it`                            | Google’s family of efficient multilingual models (1B–27B); Gemma 3 offers a 128K context window, and its larger (4B+) variants support vision input. |