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Signed-off-by: Ryan Olson <rolson@nvidia.com>
Co-authored-by: Olga Andreeva <oandreeva@nvidia.com>
Co-authored-by: Ziqi Fan <ziqif@nvidia.com>
Co-authored-by: John Thompson <jothomson@nvidia.com>
Co-authored-by: Richard Huo <rihuo@nvidia.com>
Co-authored-by: Zicheng Ma <zichengm@nvidia.com>

Signed-off-by: Ryan Olson <ryanolson@users.noreply.github.com>

Co-authored-by: Keiven Chang <keivenchang@users.noreply.github.com>

Signed-off-by: Dan Aloni <dan.aloni@vastdata.com>
Co-authored-by: Tushar Sharma <tusharma@nvidia.com>

Signed-off-by: Neelay Shah <neelays@nvidia.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: Olga Andreeva <124622579+oandreeva-nv@users.noreply.github.com>

Signed-off-by: Dan Aloni <dan.aloni@vastdata.com>

Move much of what was in the `dynamo-run` crate into `dynamo-llm` so that everyone can use it.

Example usage:

1. Create a `LocalModel`:

```
    let local_model = LocalModelBuilder::default()
	.model_path("Qwen/Qwen3-0.6B")
	.http_port(8080)
	.build().await?;
```

2. Make an engine:

```
    let engine_config = EngineConfig::StaticFull {
	engine: dynamo_engine_mistralrs::make_engine(&local_model).await?,
	model: Box::new(local_model),
    };
```

3. Connect it to an input and run it

```
    dynamo_llm::entrypoint::input::run_input(Input::Http, runtime, engine_config).await?;
```

For https://github.com/ai-dynamo/dynamo/issues/1647

Code Rabbit summary, thanks:
  * Introduced a flexible builder pattern for local model configuration, allowing advanced customization and easier initialization.
  * Added new input modes and unified input handling, supporting interactive chat, HTTP server, batch file, and distributed endpoint modes.
  * Centralized engine configuration and routing, enabling more extensible and maintainable engine management.
  * Simplified and modularized the codebase by moving input and engine logic into dedicated modules.
  * Replaced direct model construction with an asynchronous builder for improved clarity and extensibility.
  * Streamlined configuration and validation for flags and router settings.
  * Added validation to prevent incompatible input and output combinations in endpoint and dynamic modes.

Signed-off-by: PeaBrane <yanrpei@gmail.com>
Signed-off-by: Yan Ru Pei <yanrpei@gmail.com>
Signed-off-by: jain-ria <riajain@NVIDIA.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: jain-ria <riajain@NVIDIA.com>

It was removed from the docs in 0.2.1 and replaced with writing a [standalone Python engine](https://github.com/ai-dynamo/dynamo/blob/main/docs/guides/dynamo_run.md#writing-your-own-engine-in-python).

Also remove the associated `dynamo-run` feature `python`.

Releasing this in 0.3.0 will resolve #784 and #1109.

Allow both password or TLS auth, if none of these is provided fallback to no auth

Closes #657

. New mistralrs and llamacpp version
. mistralrs: Handle Gemma 3 and Llama 4 as vision models
. Update the dynamo-run docs to use Qwen 3
. Our pre-processor now supports Llama 4's newer multi-modal `config.json`
. Upgrade minijinja to handle Qwen 3's prompt template

For Llama 4 we'll need to limit the max seq len. vllm says:
> To serve at least one request with the models's max seq len (10485760), (240.00 GiB KV cache is needed,...

I was able to run Llama 4 with llamacpp and a quantized GGUF, with Dynamo doing the pre-processing.

Adding this to a Python script makes it register on the network so that `dynamo-run` can discover it and send it requests:
```
from dynamo.llm import register_llm

MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
await register_llm(endpoint, MODEL, 3)
```

Full vllm example, with pre-processing in dynamo:
- `dynamo-run in=text out=dyn://dynamo.backend.generate`
- `cd lib/bindings/python/examples/hello_world`
- `python server_vllm.py`

This builds on top of the work to move pre-processor to ingress side. It means we can decouple Rust and Python using NATS as the bus.

The `register_llm` call does this:

- Download the model from HF if necessary
- Load the model deployment card from the HF folder or extract from GGUF
- Push the tokenizer config etc into NATS object store so ingress can access it from a different machine
- Publish the model deployment card to ETCD

Part of https://github.com/ai-dynamo/dynamo/issues/743

In a distributed system we don't know if the remote workers need pre-processing done ingress-side or not. Previously Client required us to decide this before discovering the remote endpoints, which was fine because pre-processing was worker-side.

As part of moving pre-processing back to ingress-side we need to split this into two steps:
- Client discovers the endpoints, and (later PR) will fetch their Model Deployment Card.
- PushRouter will use the Model Deployment Card to decide if they need pre-processing or not, which affects the types of the generic parameters.

Part of #743