GitLab

- Setup venv

```
uv venv
source .venv/bin/activate
uv pip install pip
uv pip install sgl-kernel --force-reinstall --no-deps
uv pip install "sglang[all]==0.4.2" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/
```

- Build: `cargo build --release --features sglang`

- Run single node (make sure you're in the venv): `./tio out=sglang ~/llm_models/my_model`

- Run Deepseek multi-gpu / multi-node:

Node 1:
```
tio in=http out=sglang --model-path ~/llm_models/DeepSeek-R1-Distill-Llama-70B/ --tensor-parallel-size 8 --num-nodes 2 --node-rank 0 --dist-init-addr 10.217.98.122:9876
```

Node 2:
```
tio in=none out=sglang --model-path ~/llm_models/DeepSeek-R1-Distill-Llama-70B/ --tensor-parallel-size 8 --num-nodes 2 --node-rank 1 --dist-init-addr 10.217.98.122:9876
```

Add backend type `EngineConfig::StaticCore` that wraps the engine in a preprocessor (prompt templating and tokenization).

Add example engine `echo_core` (`out=echo_core`) which takes and returns tokens. A nice side effect is that it echos the full prompt template with system prompt, whereas `echo_full` echos only user prompt.

![image](https://github.com/user-attachments/assets/27ec0a7b-a27d-4e69-96ea-1ffa0822ea90)

Signed-off-by: Ryan McCormick <rmccormick@nvidia.com>

Signed-off-by: Neelay Shah <neelays@nvidia.com>
Co-authored-by: Ryan McCormick <rmccormick@nvidia.com>

Add support in tio for distributed components and discovery.

Node 1:
```
tio in=http out=tdr://ns/backend/mistralrs
```

Node 2:
```
tio in=tdr://ns/backend/mistralrs out=mistralrs ~/llm_models/Llama-3.2-3B-Instruct
```

This will use etcd to auto-discover the model and NATS to talk to it. You can run multiple workers on the same endpoint and it will pick one at random each time.

The `ns/backend/mistralrs` are purely symbolic, pick anything as long as it has three parts, and it matches the other node.

Signed-off-by: Ryan Olson <ryanolson@users.noreply.github.com>
Co-authored-by: Ryan McCormick <rmccormick@nvidia.com>

You can now run an HF repo directly:
```
tio ~/llm_models/Llama-3.2-1B-Instruct/
```

or a GGUF
```
tio ~/llm_models/Llama-3.2-1B-Instruct-Q4_K_M.gguf
```

Also cleanup kv_router so I can merge.

This allows us to run a real model.

Build:
```
cargo build --release --features mistralrs,cuda
```

Run:
```
./target/release/tio in=text out=mistralrs --model-path Llama-3.2-1B-Instruct-Q4_K_M.gguf
```

Why [mistral.rs](https://github.com/EricLBuehler/mistral.rs)?

- It has no dependencies. You don't need a container or a virtual env to get started.
- It supports CUDA, Metal (MacOS) and CPU-only. Everyone can join the AI revolution.
- It starts fast and serves fast (with CUDA). That makes it fun to experiment with.
- It runs many models, not just Mistral, that's just it's name.

This provides a simple example of how to write a triton-llm engine, and how to connect it to the OpenAI HTTP server.

This is the tool previously called `nio` and `llmctl`.

- **Inputs**: Text and HTTP.
- **Engines**: Echo, which streams your prompt back with a slight delay.

Build: `cargo build`

Pre-requisites: `nats-server` and `etcd` must be running locally, even though they are not yet used by `tio`.

Run with text input:
```
./target/debug/tio in=text out=echo_full --model-name test
```

Run with the triton-llm HTTP server:
```
./target/debug/tio in=http out=echo_full --http-port 8080 --model-name Echo-0B
```

List models:
```
curl localhost:8080/v1/models | jq
```

Will output
```
{
  "object": "list",
  "data": [
    {
      "id": "Echo-0B",
      "object": "object",
      "created": 1739400430,
      "owned_by": "nvidia"
    }
  ]
}
```

#### What's next

As triton-distributed gains features `tio` will be able to grow:
- When we get the pre-processor we can have token-in token-out engines. 
- When we get a pull-router we can have `in=nats` and `out=nats`.
- When we get discovery we can have dynamic engines.