aicentosUnsloth
Unsloth is a fine-tuning and local inference framework, not a chat client — why it cannot connect to aicentos
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Unsloth is an open-source framework for model fine-tuning and local inference, with a desktop app and Unsloth Studio (a browser interface). Its Connections panel looks like model provider settings, but it only accepts local or self-hosted endpoints, and cloud providers are a fixed list — there is no any OpenAI-compatible URL entry, so inference requests cannot be pointed at aicentos.
Why it cannot connect directly
- In Studio's
Settings → Connections → Add Provider, the choices are local or self-hosted services such as llama.cpp, vLLM, and Ollama, and the Base URL you fill in looks likehttp://localhost:8000/v1. - The cloud side is a fixed list (OpenAI, Anthropic, OpenRouter, ChatGPT/Codex) plus an API Key, with no entry for a custom endpoint.
- In the official docs, remote OpenAI-compatible endpoint means an inference service you deploy yourself, not a third-party aggregation gateway.
- Its direction is also server-side: Unsloth exposes an OpenAI-compatible API of its own for others to connect to.
Configuration to fill in
| Field | Value |
|---|---|
| API Key | The key created in the console |
| Base URL | https://aicentos.com/v1 |
| Model | A model name from the Model Gallery in the console |
The table above lists the generic aicentos-side parameters, for use with the alternatives below.
Alternatives
Alternative 1: Training is training, chatting is chatting
Use Unsloth for fine-tuning and local inference, and a client such as Cherry Studio or Chatbox to connect aicentos for everyday conversation. The two do not interfere with each other.
Alternative 2: Serve locally, point the front end at it
What Unsloth exposes is an OpenAI-compatible endpoint. Once the unsloth service is running locally, set the client's Base URL to its address (for example http://localhost:8000/v1) — the same set of steps as filling in aicentos.
Alternative 3: To compare results, switch the front end
To compare a locally fine-tuned model with a general model on aicentos, a client that supports several providers side by side is the least work, switching in the same interface.
Test
Once the local service is up, first curl http://localhost:8000/v1/models to confirm the endpoint is alive, then send one prompt in the client.
Notes
- Do not treat Studio's
Connectionsas a BYOK panel; what it governs is the local runtime. - Local inference is hardware-hungry; for long-context and high-volume tasks, prefer aicentos.
- The official docs describe no workflow for generating training data with a third-party API, so do not plan a pipeline around that idea.