GitLab

```
dynamo-run in=batch:prompts.jsonl out=mistralrs ~/llm_models/Llama-3.2-3B-Instruct/
```

The file has genai format, one entry per line:
```
{"text": "the prompt"}
{"text": ..etc
```

The prompt is evaluated and the output written to `output.jsonl` in the
same folder as the input.

At the end of the run various statistics are printed:
> Ran 5 files in 8s 679ms. Tokens in: 40 (5/s). Tokens out: 346 (43/s)

This is also helpful for pushing load into the system and stressing the
various components. Not intended for performance measurement, it's a
batch inference tool.

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.

- Any engine can take the name of a Hugging Face repository. It will be downloaded before calling the engine.

- The default engine (previously always mistralrs) depends on what is compiled in.

- Text can be piped in and will result in a single run of the model.

All of those together mean if you build with `--features vllm` you can do this and it will download the model and run it with vllm, answer your question, and exit:
```
echo "What is the capital of Costa Rica?"  | dynamo-run Qwen/Qwen2.5-3B-Instruct
```
Co-authored-by: Ryan McCormick <rmccormick@nvidia.com>

If the python file raises an exception we print it like Python would.

```
$ ./target/debug/dynamo-run in=http out=pystr:~/Temp/cn47/1_e.py --model-name test

Traceback (most recent call last):
  File "/home/graham/Temp/cn47/1_e.py", line 17, in generate
    raise MyException("The message")
1_e.MyException: The message
```

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