Instructions to use xxvoidsxx/koe-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xxvoidsxx/koe-270m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xxvoidsxx/koe-270m")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xxvoidsxx/koe-270m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use xxvoidsxx/koe-270m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xxvoidsxx/koe-270m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xxvoidsxx/koe-270m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/xxvoidsxx/koe-270m
- SGLang
How to use xxvoidsxx/koe-270m with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "xxvoidsxx/koe-270m" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xxvoidsxx/koe-270m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "xxvoidsxx/koe-270m" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xxvoidsxx/koe-270m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use xxvoidsxx/koe-270m with Docker Model Runner:
docker model run hf.co/xxvoidsxx/koe-270m
koe-270m
koe (ε£°, "voice") β xvoidsx's family of small voice-assistant models. The voice of the wired, built for Hey Lain and local-first AI on open hardware.
At a glance
| Params | ~270M |
| Quantized | ~200 MB (Q4_K_M) |
| License | Apache 2.0 (intended) |
| Runs on | Raspberry Pi, 2015-era laptops, anything |
Which variant should I pick?
| Variant | Size | For |
|---|---|---|
koe-270m (this) |
~200 MB Q4 | Weakest hardware β the perma-computing floor |
koe-600m (planned) |
~400 MB Q4 | Everyday laptops, navi mini |
koe-1b (planned) |
β | Machines with headroom |
Same voice, different weight classes. Every variant ships as GGUF for ollama:
ollama pull hf.co/xxvoidsxx/koe-270m
How this model was made
In progress β training data (wired-voice conversations) is being curated. This section will document the full recipe: base model, dataset, hyperparameters, and validation, the way research should be documented.
Benchmarks
To come. Validated on real old hardware, because that's the point β if it runs on a 2015 laptop, it runs anywhere.
Built by xvoidsx β researching local AI on open hardware.