GitLab

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>

What does the PR do?
 - adds etcd method to atomic create or validate a kv entry.
 - adds integration tests to validate the behavior

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

-   Minor update to DeadlineStream
-   Adding tests

Enables `#[tokio::test]` via `Runtime::from_current()`

This uses the current handle as both the primary and secondary.

Co-authored-by: hongkuanz <hongkuanz@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>

Co-authored-by: Sean Choi <choishsean@gmail.com>
Co-authored-by: aflowers <aflowers@nvidia.com>

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

Co-authored-by: Biswa Ranjan Panda <biswaranjanp@nvidia.com>

Signed-off-by: Piotr Marcinkiewicz <piotrm@nvidia.com>
Co-authored-by: Piotr Marcinkiewicz <piotrm@nvidia.com>
Co-authored-by: Ryan McCormick <rmccormick@nvidia.com>

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

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

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

Upgrade mistralrs to latest.

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.

Signed-off-by: Piotr Marcinkiewicz <piotrm@nvidia.com>
Co-authored-by: Piotr Marcinkiewicz <piotrm@nvidia.com>
Co-authored-by: Neelay Shah <neelays@nvidia.com>