feat(ai): complete GPU deployment with self-hosted Qwen 2.5 7B model

This commit finalizes the GPU infrastructure deployment on RunPod:

- Added qwen-2.5-7b model to LiteLLM configuration
  - Self-hosted on RunPod RTX 4090 GPU server
  - Connected via Tailscale VPN (100.100.108.13:8000)
  - OpenAI-compatible API endpoint
  - Rate limits: 1000 RPM, 100k TPM

- Marked GPU deployment as COMPLETE in deployment log
  - vLLM 0.6.4.post1 with custom AsyncLLMEngine server
  - Qwen/Qwen2.5-7B-Instruct model (14.25 GB)
  - 85% GPU memory utilization, 4096 context length
  - Successfully integrated with Open WebUI at ai.pivoine.art

Infrastructure:
- Provider: RunPod Spot Instance (~$0.50/hr)
- GPU: NVIDIA RTX 4090 24GB
- Disk: 50GB local SSD + 922TB network volume
- VPN: Tailscale (replaces WireGuard due to RunPod UDP restrictions)

Model now visible and accessible in Open WebUI for end users.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

ai/litellm-config.yaml

@@ -24,6 +24,15 @@ model_list:
       model: anthropic/claude-3-haiku-20240307
       api_key: os.environ/ANTHROPIC_API_KEY
 
+  # Self-hosted model on GPU server via Tailscale VPN
+  - model_name: qwen-2.5-7b
+    litellm_params:
+      model: openai/qwen-2.5-7b
+      api_base: http://100.100.108.13:8000/v1
+      api_key: dummy
+      rpm: 1000
+      tpm: 100000
+
 litellm_settings:
   drop_params: true
   set_verbose: false  # Disable verbose logging for better performance