asepharyana-hub-llm-api/README.md

llm-api

OpenAI-compatible LLM inference server using llama-cpp-2 (Rust).

Model: MiniCPM-V-4.6 Q4_K_M (505 MB)
Engine: llama.cpp via llama-cpp-2 crate
Domain: ai.asepharyana.my.id

API

GET /health

{"status": "ok", "model": "minicpm-v-4.6-q4_k_m"}

GET /v1/models

OpenAI-compatible model listing.

POST /v1/chat/completions

OpenAI-compatible chat completions.

curl https://ai.asepharyana.my.id/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minicpm-v-4.6",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 100
  }'

Development

# Build
cargo build --release

# Run (with model path env var)
MODEL_PATH=/path/to/model.gguf ./target/release/llm-api

# Or use default path
./target/release/llm-api

Docker

docker compose -f docker-compose.yml up -d

Benchmark

Framework	Model Size	tok/s	vs PyTorch
PyTorch BF16	2.48 GB	0.97	1.0x
llama.cpp Q4_K_M	505 MB	39.1	40.3x 🏆

Infrastructure

• Traefik router: ai.asepharyana.my.id → llm-api:8080
• Network: app-shared-net
• Docker Compose: see llm-api.yml