services/vllm/config_bge.yaml

model: BAAI/bge-large-en-v1.5
host: "0.0.0.0"
port: 8002
uvicorn-log-level: "info"
gpu-memory-utilization: 0.06
dtype: float16
task: embed
feat: add BGE embedding model for concurrent operation with Llama - Create config_bge.yaml for BAAI/bge-large-en-v1.5 on port 8002 - Reduce Llama VRAM to 70% and context to 16K for concurrent use - Add BGE service to supervisor with vllm group 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> 2025-11-30 19:55:13 +01:00			`model: BAAI/bge-large-en-v1.5`
			`host: "0.0.0.0"`
			`port: 8002`
			`uvicorn-log-level: "info"`
fix: adjust VRAM for 24K context based on actual usage Based on error output, model uses ~17.5GB (not 15GB estimated). - Llama: 85% VRAM for 24576 context (3GB KV cache) - BGE: 6% VRAM (reduced to fit) - Total: 91% 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> 2025-11-30 22:46:34 +01:00			`gpu-memory-utilization: 0.06`
feat: add BGE embedding model for concurrent operation with Llama - Create config_bge.yaml for BAAI/bge-large-en-v1.5 on port 8002 - Reduce Llama VRAM to 70% and context to 16K for concurrent use - Add BGE service to supervisor with vllm group 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> 2025-11-30 19:55:13 +01:00			`dtype: float16`
			`task: embed`