|
Download runtime-baseline.md from broadfield/openjev-cpu-deploy-kit: direct link, hf CLI and curl.
- Browser
- Download file 2.13 kB
-
https://huggingface.co/datasets/broadfield/openjev-cpu-deploy-kit/resolve/main/runtime-baseline.md
- Command line
-
hf download hf://datasets/broadfield/openjev-cpu-deploy-kit/runtime-baseline.md
-
curl -L -o runtime-baseline.md https://huggingface.co/datasets/broadfield/openjev-cpu-deploy-kit/resolve/main/runtime-baseline.md
2.13 kB
| # OpenJev CPU runtime — baseline Space spec | |
| The recommended zero-GPU Hugging Face Space configuration for | |
| **broadfield/openjev-cpu-run** (the one-click Duplicate target). | |
| | setting | value | why | | |
| |---|---|---| | |
| | SDK | **Docker** | we need llama.cpp binaries + a multi-process entrypoint (bash) | | |
| | Hardware | **CPU basic (free)** | zero GPU, by design (note: Docker Spaces on cpu-basic require a PRO subscription) | | |
| | Persistent storage | **30 GB** (≈ $0.50/mo) | `/data` holds the 16.2 GB GGUF + tokenizer | | |
| | Auto-sleep | any (e.g. 15 min) | wake-up reloads model from `/data` → ~1–2 min, not 20+ | | |
| | Visibility | public (default) | the API is meant to be called; flip private + `OPENJEV_TOKEN` to lock it | | |
| | Secrets | none required | optional `OPENJEV_TOKEN` via Space settings → will appear as an env var | | |
| ## Approximate resource/memory math (Q4_K_M) | |
| | item | size | | |
| |---|---| | |
| | `OpenJev-Q4_K_M.gguf` on disk | 16.2 GB | | |
| | RAM to hold the model weights | ~16.2 GB | | |
| | KV cache (llama.cpp fork, 8-bit, 8192 ctx, 1 stream) | ~1–2 GB | | |
| | openjev-server + transformers tokenizer | ~1–2 GB | | |
| | **peak RSS** | **~18–20 GB** (fits the 16 vCPU / 32 GB free tier) | | |
| If the free tier's memory turns out tight (it runs 32 GB RAM / 16 vCPU per | |
| current info), drop to `LLAMA_CTX=4096` or use quant `OpenJev-Q5_K_M.gguf`. | |
| ## Build-time budget | |
| The image does, in order: | |
| 1. `apt` build tools — ~1 min | |
| 2. clone + checkout llama.cpp `b4630` — ~1 min | |
| 3. **CMake CPU build of llama-server** — **~15–35 min** on the build machine | |
| 4. `pip install openjev-server` (transformers etc.) — ~2–4 min | |
| 5. final image copy — ~1 min | |
| First **boot** then downloads the GGUF (~16.2 GB) into persistent `/data` — | |
| that's the long pole (20–50 min depending on HF Hub egress). Persisted | |
| afterward, so subsequent boots are 1–2 min. | |
| ## Why entrypoint waits up to 15 min for llama-server | |
| A cold boot loads 16.2 GB off disk into RAM and runs llama.cpp's warmup; on a | |
| shared CPU that's minutes. The entrypoint polls `/v1/models` until ready, then | |
| starts openjev-server, so HF's health check never sees a crash while loading. |