Architecture for Jan to support multiple Inference Engines

[0xSage]

Previous thread: #771

Context

• Jan's architecture will default to Nitro, but have flexibility to incorporate other Model Backends / Inference Engines
• We see very fast movement around a few ecosystems
  • Intel BigDL
  • Intel Extensions for Transformers
  • TensorRT-LLM for WIndows
  • And many more to come
• Jan's Architecture needs to be able to incorporate modular Inference Engines
  • Parallelize efforts to support different inference engines
  • Allow us to hedge technical architectural risks

Solution

I envision an architecture in Jan that has the following:

• Model Extension
• Inference Extension
• [Many] Extensions for each Inference Engine

Models Extension

• Handles Models filesystem and CRUD
• /models API endpoint

Inference Extension

• Handles generic endpoints (for now, /chat/completions, later /audio/speech)
• Routes an inference request to the correct Inference Engine extension (defined by model.json)

Extension for each Inference Engine

• Each Inference Engine exposes a /chat/completions endpoint
• Each Inference Engine is expected to have its own OpenAI compliant API
• Each Inference Engine is expected to do its own automatic resource management (e.g. loading, unloading)
• Each Inference Engine is expected to stream events to the Frontend UI
  • e.g. Model Loading, Unloading events (i.e. "please wait")
  • e.g. SSEs for model response
• Extensions for Inference Engine are available via The Hub
  • These are NPM packages that build on our Extensions API

Example

File Tree

/jan
    /models
        /llama2-70b
             llama2-gguf-q4_k_m.bin     # uses Nitro
             model.json
        /llama2-70b-intel-bigdl
              #pytorch files
              model.json
    /engines
        /nitro
            engine.json
        /openai

model.json gpt4-32k-1603

{
    engine: "openai"    // If not specified, defaults to Nitro
}

engine.json example for Nitro

{
    settings: { ...default_gguf_settings},
    parameters: { ...default_gguf_params},
}

Execution Path

1. User makes inference request to llama2-70b-intel-bigdl
2. Inference Extension loads the model.json for llama2-70b-intel-bigdl and sees engine is intel-bigdl
3. Inference Extension routes it to intel-bigdl Inference Engine Extension
4. intel-bigdl Inference Engine Extension takes in /chat/completions request, runs inference, and returns result through SSE