feat: Add recipe-based one-click model deployment system

Introduces a YAML recipe system for simplified model deployment:

- run-recipe.py: Main script handling build, download, and launch
- run-recipe.sh: Bash wrapper for dependency management
- recipes/: Pre-configured recipes for common models
  - glm-4.7-flash-awq.yaml: GLM-4.7-Flash with AWQ quantization
  - glm-4.7-nvfp4.yaml: GLM-4.7 with NVFP4 (cluster-only)
  - minimax-m2-awq.yaml: MiniMax M2 with AWQ
  - openai-gpt-oss-120b.yaml: OpenAI GPT-OSS 120B with MXFP4

Key features:
- Auto-discover cluster nodes with --discover, saves to .env
- Load nodes from .env automatically on subsequent runs
- cluster_only flag for models requiring multi-node setup
- build_args field for Dockerfile selection (--pre-tf, --exp-mxfp4)
- Solo mode auto-strips --distributed-executor-backend ray
- --setup flag for full build + download + run workflow
- --dry-run to preview execution without running

Usage:
  ./run-recipe.sh --discover           # Find and save cluster nodes
  ./run-recipe.sh glm-4.7-flash-awq --solo --setup
  ./run-recipe.sh glm-4.7-nvfp4 --setup  # Uses nodes from .env

recipes/minimax-m2-awq.yaml

@@ -0,0 +1,40 @@
+# Recipe: MiniMax-M2-AWQ
+# MiniMax M2 model with AWQ quantization
+
+recipe_version: "1"
+name: MiniMax-M2-AWQ
+description: vLLM serving MiniMax-M2-AWQ with Ray distributed backend
+
+# HuggingFace model to download (optional, for --download-model)
+model: QuantTrio/MiniMax-M2-AWQ
+
+# Container image to use
+container: vllm-node
+
+# No mods required
+mods: []
+
+# Default settings (can be overridden via CLI)
+defaults:
+  port: 8000
+  host: 0.0.0.0
+  tensor_parallel: 2
+  gpu_memory_utilization: 0.7
+  max_model_len: 128000
+
+# Environment variables
+env: {}
+
+# The vLLM serve command template
+command: |
+  vllm serve QuantTrio/MiniMax-M2-AWQ \
+      --port {port} \
+      --host {host} \
+      --gpu-memory-utilization {gpu_memory_utilization} \
+      -tp {tensor_parallel} \
+      --distributed-executor-backend ray \
+      --max-model-len {max_model_len} \
+      --load-format fastsafetensors \
+      --enable-auto-tool-choice \
+      --tool-call-parser minimax_m2 \
+      --reasoning-parser minimax_m2_append_think