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Co-authored-by: Graham King <grahamk@nvidia.com>

`cargo build --locked` won't let you use "1.85.0" if you only have "stable" installed, even if those are the same thing right now.

Signed-off-by: Harrison Saturley-Hall <454891+saturley-hall@users.noreply.github.com>
Signed-off-by: Meenakshi Sharma <163925564+nvda-mesharma@users.noreply.github.com>
Co-authored-by: Meenakshi Sharma <163925564+nvda-mesharma@users.noreply.github.com>

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

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

Signed-off-by: Meenakshi Sharma <163925564+nvda-mesharma@users.noreply.github.com>

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

Signed-off-by: Meenakshi Sharma <163925564+nvda-mesharma@users.noreply.github.com>
Co-authored-by: Anant Sharma <anants@nvidia.com>

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.

Co-authored-by: Ryan McCormick <rmccormick@nvidia.com>

Signed-off-by: Meenakshi Sharma <163925564+nvda-mesharma@users.noreply.github.com>

Signed-off-by: Meenakshi Sharma <163925564+nvda-mesharma@users.noreply.github.com>