GitLab

This PR modifies the mistralrs engine to ensure that the maximum output token length never exceeds the context length provided.

Signed-off-by: Hongkuan Zhou <tedzhouhk@gmail.com>
Co-authored-by: jothomson <jwillthomson19@gmail.com>
Co-authored-by: Ryan McCormick <rmccormick@nvidia.com>

Example:
```
dynamo-run out=<engine> <model> --kv-cache-block-size 64
```

In a distributed system this goes on the worker node and is propagated to ingress via the model deployment card.

Previously hard coded to 16, which is now the default.

- Load context_length from model. Closes #1172
- Store context length and KV cache block size in Model Deployment Card #1170

Llama 4 has a very large context length (aka n_ctx, model_max_length, max_model_len), and vllm won't start unless it can allocate enough KV cache for the entire context.

Allow passing `--context-length <N>` to `dynamo-run` to limit it so long-context models will fit.

Future todo:
- Restrict every request's `max_tokens` to below the context length. Our pre-processor should do this by setting stop_conditions.max_tokens. mistralrs engine wrapper must do it itself because it does not use the pre-processor.
- mistralrs and llamacpp currently have a hard-coded max context length if one is not provided on the command line. Change those to be the model's built-in max, read from the GGUF or tokenizer_config.json.

Implements OpenAI embeddings (interface only).

- Adds ModelType::Embedding
- Adds OpenAI embedding request/response structs
- Adds support for embedding model discovery

. 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.

Signed-off-by: Olga Andreeva <124622579+oandreeva-nv@users.noreply.github.com>

Send a warm‑up request to the mistralrs engine so that subsequent requests are faster.

Moved all of `lib/llm/src/engines` to their own crates as e.g. `lib/engines/mistralrs`. This will allow publishing of the `dynamo-llm` crate as it won't have any github dependencies.

The only engines in dynamo-llm will be the demo `echo` ones.
Co-authored-by: Graham King <grahamk@nvidia.com>

Under load it sometimes drops a request. The request gets added to the batch (sequence) and immediately gets a FinishReason Stop. Not sure why. It doesn't happen with the default scheduler (non-paged attention), so switch to that for now.

Previously we tokenized and counted tokens to stop when max tokens was reached. Now we let the mistral.rs engine do it which saves the extra tokenization step.

Also dynamo-run prints which engines are compiled in in help message, and some minor lint fixes.

- Latest from repo, many improvements
- Support most of the OpenAI request features (temperature, top_p, etc)
- Download models from Hugging Face if necessary

Co-authored-by: Biswa Panda <biswa.panda@gmail.com>

Fixes a panic.

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

Docs in README

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

Signed-off-by: Neelay Shah <neelays@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.

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.