骆驼

Build from source

Camelid uses the toolchain pinned in rust-toolchain.toml. The React/Vite web UI in frontend/ is embedded in the engine binary.

(cd frontend && npm ci && npm run build)
cargo build --release --locked --bin camelid

See the contributor quick start for prerequisites and development setup.

Quick start

Model downloads are typically 1–8 GB. For the simplest setup, install the desktop app and download a model from its Models page.

OpenAI-compatible API

camelid serve exposes the browser UI and API on the same port. Read the loaded model ID from GET /v1/models, then call the chat-completions endpoint:

curl http://127.0.0.1:8181/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Llama 3.2 3B Instruct",
    "messages": [{"role": "user", "content": "Why is local inference useful?"}],
    "max_tokens": 128,
    "temperature": 0
  }'

Camelid also supports /v1/responses, /v1/embeddings, /v1/rerank, streaming, local image input on supported VLM rows, function tools, structured text formats, conversations, and optional local SQLite storage. The machine-readable route and feature inventory is available from /api/capabilities.

Supported models

Start with one of these five options. Support applies to exact model files, quantizations, and tested execution paths; see the compatibility ledger for each model's limits.

Llama 3.2 3B and LFM2.5 have support limited to the documented exact-file smoke tests; broader context, hardware, and behavior do not inherit that support.

Browse the full catalog and validation details, including coding, vision, and experimental models. Gemma 4 12B-It QAT Q4_0 is available for a tested 16 GB M4 Mac, with validation pending. Download availability does not imply verified support.

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For image chat and embeddings and reranking, see the model guide for supported files and setup.