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+# Qwen3.6-35B-A3B Bootstrap Issue
+
+## Problem
+vllm 0.6.3+corex.3.2.3 does not recognize `qwen3_5_moe` model type.
+
+```
+ValueError: The checkpoint you are trying to load has model type `qwen3_5_moe` 
+but Transformers does not recognize this architecture.
+```
+
+## Root Cause
+- Model `config.json` specifies `"model_type": "qwen3_5_moe"` and `"architectures": ["Qwen3_5MoeForCausalLM"]`
+- Server transformers version: 4.51.3 (needs ≥ 4.57.1)
+- Server vllm version: 0.6.3+corex.3.2.3
+
+## Model Architecture (from config.json)
+- **Type**: Qwen3_5MoeForCausalLM (MoE with linear attention)
+- **Total params**: ~35B
+- **Active params per token**: ~3B (8 of 256 experts)
+- **Hidden size**: 2048
+- **Layers**: 40 (30 linear_attention + 10 full_attention, every 4th is full)
+- **Experts**: 256 total, 8 per token
+- **Expert intermediate**: 512
+- **Shared expert intermediate**: 512
+- **Head dim**: 256
+- **KV heads**: 2 (GQA ratio 8:1)
+- **Max position**: 262144
+- **Vocab**: 248320
+- **Precision**: bfloat16
+- **Linear attention**: conv kernel dim=4, 16 key heads (dim128), 32 value heads (dim128)
+- **MTP**: 1 hidden layer (multi-token prediction)
+- **Vision**: yes (patch16, depth27, hidden1152)
+
+## Key Architecture Features
+1. **Hybrid attention**: 3 linear_attention + 1 full_attention pattern (30+10=40 layers)
+2. **MoE**: 256 experts, top-8 routing = very sparse
+3. **Linear attention with conv**: NOT standard transformer — uses conv kernel dim=4
+4. **Multi-token prediction (MTP)**: 1 extra hidden layer for speculative prediction
+5. **Multimodal**: has vision encoder (but competition likely tests text only)
+
+## Solution Paths
+1. **EngineX route**: Check if the competition's enginex-vllm package already supports this model
+   - The repo has `enginex-vllm-bi100-qwen36-main.zip` (96MB) — THIS is likely the answer
+2. **Upgrade transformers**: `pip install transformers>=4.57.1` (may break corex compatibility)
+3. **Custom model registration**: Register Qwen3_5MoeForCausalLM in vllm's model registry