Instructions to use elizaos/eliza-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use elizaos/eliza-1 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf elizaos/eliza-1:Q4_K_M # Run inference directly in the terminal: llama cli -hf elizaos/eliza-1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf elizaos/eliza-1:Q4_K_M # Run inference directly in the terminal: llama cli -hf elizaos/eliza-1:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf elizaos/eliza-1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf elizaos/eliza-1:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf elizaos/eliza-1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf elizaos/eliza-1:Q4_K_M
Use Docker
docker model run hf.co/elizaos/eliza-1:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use elizaos/eliza-1 with Ollama:
ollama run hf.co/elizaos/eliza-1:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use elizaos/eliza-1 with Docker Model Runner:
docker model run hf.co/elizaos/eliza-1:Q4_K_M
- Lemonade
How to use elizaos/eliza-1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull elizaos/eliza-1:Q4_K_M
Run and chat with the model
lemonade run user.eliza-1-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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Download README.md from elizaos/eliza-1: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/elizaos/eliza-1/resolve/50cffb075ae3c24a4b0cd3a8ccdfaa92506f70d4/README.md
- Command line
-
hf download hf://elizaos/eliza-1@50cffb075ae3c24a4b0cd3a8ccdfaa92506f70d4/README.md
-
curl -L -o README.md https://huggingface.co/elizaos/eliza-1/resolve/50cffb075ae3c24a4b0cd3a8ccdfaa92506f70d4/README.md
2.8 kB
| license: other | |
| library_name: gguf | |
| tags: | |
| - gguf | |
| - qwen | |
| - eliza-1 | |
| - local-inference | |
| # Eliza-1 | |
| Eliza-1 is the single elizaOS local-inference model repository. The v1 bundles are raw/base Qwen-lineage GGUF weights converted and packaged for the Eliza local harness; they are not fine-tuned. | |
| Runtime bundles live under `bundles/<tier>/` so the app can resolve the catalog manifest and all component files from one repo. | |
| APOLLO is the required optimizer for later fine-tuned releases. It keeps optimizer state small enough for full-parameter training on smaller GPUs, so publish scripts should not introduce a second optimizer path. | |
| ## Bundle Matrix | |
| | Tier | Remote path | Status | Files | Size | Voice note | | |
| | --- | --- | --- | ---: | ---: | --- | | |
| | 27b-256k | `bundles/27b-256k/` | ready | 55 | 20.94 GiB | policy satisfied or not declared | | |
| Quantization policy: Q4_K_M is the default published runtime artifact today; Q6_K and Q8_0 are tracked in the app catalog as higher-precision variants for the hardware optimizer to select when those files are published. | |
| ## Voice Modules | |
| The voice sub-models live under `voice/<sub>/`. The unified layout | |
| replaces the legacy `elizaos/eliza-1-voice-*` per-component repos | |
| (consolidated 2026-05-15). Each sub-path carries a `provenance.txt` | |
| linking back to its source repo at the time of consolidation. | |
| | Sub-path | Contents | Source repo (deleted) | | |
| | --- | --- | --- | | |
| | `voice/omnivoice/` | OmniVoice TTS — `omnivoice-base-q4_k_m.gguf`, `omnivoice-base-q8_0.gguf`, `omnivoice-tokenizer-q4_k_m.gguf`, presets including `voice-preset-same.bin` and `voice-preset.elz2` (same-voice v0.1) plus README/eval/manifest | `elizaos/eliza-1-voice-omnivoice-same-v01` | | |
| | `voice/emotion/` | Wav2Small emotion classifier — `wav2small-cls7-int8.onnx` (cls7 head) plus its config JSON, `eval.json`, `manifest.json`, README | `elizaos/eliza-1-voice-emotion` | | |
| | `voice/turn/` | LiveKit turn detector — `intl/turn-detector-intl-q8.gguf` (281 MB), `intl/model_q8.onnx` (262 MB), tokenizer assets, README, manifest | `elizaos/eliza-1-voice-turn` | | |
| | `voice/asr/` | Reserved (legacy repo was empty); ASR weights live under the canonical `voice/asr/` path: `eliza-1-asr-q8_0.gguf`, `eliza-1-asr-mmproj.gguf` | `elizaos/eliza-1-voice-asr` | | |
| | `voice/kokoro/` | Kokoro TTS — extra preset `voices/af_sam.bin` (the canonical Kokoro weights live alongside under `voice/kokoro/kokoro-v1.0-q4.onnx` and `voices/af_bella.bin`, `voices/af_same.bin`) | `elizaos/eliza-1-voice-kokoro` | | |
| Other voice components (`voice/diarizer/`, `voice/wakeword/`, `voice/speaker-encoder/`, `voice/vad/`, `voice/embedding/`, `voice/turn-detector/`, `voice/voice-emotion/`) were already shipping under the unified repo and remain unchanged. | |
| Repository: `elizaos/eliza-1` | |