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CHIM Large Language Model Services

These are the current large language model services exposed in HerikaServer for CHIM.

Services

Local Models

CHIM can use LM Studio inside DwemerDistro through the launcher's LLM Studio component, or connect to a separate LM Studio or KoboldCPP server. Follow the Local LLM Guide for the complete setup.

OpenRouter

OpenRouter is the easiest general starting point for most CHIM users. It gives you one account that can access many different AI models, so you can try options without constantly rebuilding your setup.

OpenRouter

OpenAI

OpenAI connects CHIM straight to GPT models. It is a simple choice if you already use OpenAI and want a more direct setup instead of browsing lots of model options.

OpenAI

Google

Google uses Gemini models for your CHIM conversations. It is another mainstream cloud option if you prefer Google's AI tools or want to compare its style against OpenAI and OpenRouter.

Google AI Studio

Groq

Groq is mainly about speed. It can make CHIM feel more responsive because replies tend to come back quickly, which some players prefer for fast back-and-forth dialogue.

Groq

Nano-GPT

Nano-GPT is another hosted service that gives you access to AI models without running them on your own PC. Think of it as an alternative provider if you want more choice outside the biggest platforms.

Nano-GPT

Player2

Player2 is for people who want CHIM to talk to a local AI app on their own machine. It is useful if you want more control over what model you use, but it is usually less beginner-friendly than cloud services.

Player2 Local Endpoint

Use Your Own LLM Provider

OpenRouter is optional. CHIM can connect to other hosted or local services through a Custom connector.

Before You Start

Get these details from your provider:

  • An API key, unless the service runs locally without one.
  • The complete chat-completions URL.
  • The exact model name the provider expects.

Create the Connector

  1. Open API Keys. Save the provider's key in a preset entry, or add a named custom key.
  2. Open Large Language Models, select New Connector, then choose Custom.
  3. Enter a clear Name, the complete URL, and the provider's exact Model name.
  4. Choose the matching Driver. Common choices include OpenAI JSON for OpenAI-compatible services, Google OpenAI JSON for Gemini, and Groq JSON for Groq.
  5. Select the API key you saved.
  6. Leave Reasoning Model Fix, Enforce JSON, JSON Schema, Prefill JSON, and Disable Streaming unchanged unless the provider specifically requires them.
  7. Click Save, then Test. Do not assign the connector until the test succeeds.

Example: Google Gemini

Gemini is one example of a provider you can add manually:

  • API key: Save your Google AI Studio key under the Google preset or a named custom key.
  • URL: https://generativelanguage.googleapis.com/v1beta/openai/chat/completions
  • Driver: Google OpenAI JSON
  • Model: Use the exact current model name shown in Google AI Studio.

The built-in Google connector is simpler for most users. Use Custom when you want manual control over the endpoint and model. See Google's OpenAI compatibility guide for its current endpoint and model guidance.

Use It in CHIM

Open Profiles and select the connector for Standard, Fast, Powerful, Experimental, Diary, or Formatter. For shared work such as summaries, memories, Background Life, Director Mode, relationships, or Oghma, assign it under Global Settings - Global Connectors.

If the Test Fails

  • 401 or 403: Check the selected API key and the provider account.
  • 404: Check the full URL and model name.
  • No usable response: Make sure the selected driver matches the provider's response format.
  • Invalid JSON: Try a model that handles structured responses well. Only enable the advanced JSON options when the provider supports them.

Models For Other Tasks

Global Connectors lets you choose separate models for Director, Background Life, dynamic profiles, memories, scene classification and quests. Quest Creation Connector and Quest Engine Connector are separate choices.

Use Local LLMs when you want to run a model on your own computer.