services/vllm/config_llama.yaml

model: meta-llama/Llama-3.1-8B-Instruct
host: "0.0.0.0"
port: 8001
uvicorn-log-level: "info"
gpu-memory-utilization: 0.85
max-model-len: 24576
dtype: auto
enforce-eager: false
enable-auto-tool-choice: true
tool-call-parser: "llama3_json"
Initial commit 2025-11-26 15:31:41 +01:00			`model: meta-llama/Llama-3.1-8B-Instruct`
			`host: "0.0.0.0"`
			`port: 8001`
			`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: 0.85`
feat: balance Llama 24K context with concurrent BGE Adjusted VRAM allocation for concurrent operation: - Llama: 80% VRAM, 24576 context - BGE: 8% VRAM - Total: 88% of 24GB RTX 4090 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> 2025-11-30 22:39:08 +01:00			`max-model-len: 24576`
fix: remove vllm embedding 2025-11-27 01:12:57 +01:00			`dtype: auto`
			`enforce-eager: false`
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			`enable-auto-tool-choice: true`
			`tool-call-parser: "llama3_json"`