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twinny

Inference providers

These example configurations serve as a starting point. Individual adjustments may be required depending on your specific hardware and software environments.

Note: twinny chat (not auto-complete) should be compatible with any API which adheres to the OpenAI API specification.

Ollama (Configured by default)

Auto-complete

  • Hostname: localhost
  • Port: 11434
  • Path: /api/generate
  • Model Name: codellama:7b-code
  • FIM Template: codellama

Chat

  • Hostname: localhost
  • Port: 11434
  • Path: /v1/chat/completions
  • Model Name: codellama:7b-instruct

Open WebUI using Ollama

Open WebUI can be used a proxy Ollama, simply configure the endpoint to match what is served by OpenWeb UI.

Auto-complete

  • Hostname: localhost
  • Port: The port OpenWebUI is serving, typically 8080 or 3000.
  • Path: /ollama/api/generate
  • Model Name: codellama:7b-code
  • FIM Template: Select a template that matches the model, such as codellama for codellama:7b-code or deepseek for deepseek-coder.

Chat

  • Hostname: localhost
  • Port: The port OpenWebUI is serving, typically 8080 or 3000.
  • Path: /ollama/v1/chat/completions
  • Model Name: codellama:7b-instruct or any effective instruct model.

LM Studio

Auto-complete

  • Hostname: localhost
  • Port: 1234
  • Path: /v1/completions
  • Model Name: Base model such as codellama-7b.Q5_K_M.gguf
  • LM Studio Preset: CodeLlama Completion
  • FIM Template: Select a template that matches the model, such as codellama for CodeLlama-7B-GGUF or deepseek for deepseek-coder:6.7b-base-q5_K_M.

Chat

  • Hostname: localhost
  • Port: 1234
  • Path: /v1/chat/completions
  • Model Name: codellama:7b-instruct or your preferred instruct model.
  • LM Studio Preset: Default or CodeLlama Instruct

LiteLLM

Auto-complete

LiteLLM technically supports auto-complete using the custom-template FIM template, and by editing the fim.hbs file, however result will vary depending on your model and setup.

Chat

  • Hostname: localhost
  • Port: 4000
  • Path: /v1/chat/completions

Start LiteLLM with the following command:

Terminal window
litellm --model gpt-4-turbo

Llama.cpp

Auto-complete

Start Llama.cpp in the terminal with this Docker command:

For example using Docker and codellama-7b.Q5_K_M.gguf

Terminal window
docker run -p 8080:8080 --gpus all --network bridge -v /path/to/your/models:/models local/llama.cpp:full-cuda --server -m /models/codellama-7b.Q5_K_M.gguf -c 2048 -ngl 43 -mg 1 --port 8080 --host 0.0.0.0

Configure your provider settings as follows:

  • Hostname: localhost
  • Port: 8080
  • Path: /completion
  • FIM Template: Select a template that matches the model, such as codellama for CodeLlama-7B-GGUF or deepseek for deepseek-coder:6.7b-base-q5_K_M.

Chat

The performance of chat functionalities with Llama.cpp has been mixed. If you obtain favorable results, please share them by opening an issue or a pull request.

  • Hostname: localhost
  • Port: 8080
  • Path: /completion
  • Model Name: CodeLlama-7B-GGUF or any other strong instruct model.

Oobabooga

Terminal window
bash start_linux.sh --api --listen

Auto-complete

Navigate to http://0.0.0.0:7860/ and load your model:

  • Hostname: localhost
  • Port: 5000
  • Path: /v1/completions
  • Model Name: CodeLlama-7B-GGUF or another effective instruct model.
  • FIM Template: Select a template that matches the model, such as codellama for CodeLlama-7B-GGUF or deepseek for deepseek-coder:6.7b-base-q5_K_M.

Chat

Chat functionality has not been successful on Linux with Oobabooga:

  • Hostname: localhost
  • Port: 5000
  • Path: /v1/chat/completions
  • Model Name: CodeLlama-7B-GGUF

Symmetry

Symmetry is a decentralized tool that allows you to connect to a network of computational resources. It can be used as an inference provider for twinny, offering access to a variety of models through its peer-to-peer network.

Using Symmetry

  1. In the twinny extension settings, select your desired model.
  2. Click the “Connect to Symmetry” button in the extension.
  3. The extension will automatically connect to the Symmetry network using the selected model.

This streamlined process allows you to easily tap into the Symmetry network without manual configuration.

Note: When using Symmetry, be aware that your requests are processed by other nodes in the network. Consider the sensitivity of your data and choose trusted providers when necessary.