Add tkml-llm to top readme

README.md

@@ -6,8 +6,12 @@
 
 We are on a mission to make it easy to use the most important tools in the ONNX ecosystem. TurnkeyML accomplishes this by providing a no-code CLI, `turnkey`, as well as a low-code API, that provide seamless integration of these tools.
 
+We also provide [`turnkey-llm`](https://github.com/onnx/turnkeyml/tree/main/src/turnkeyml/llm), which has LLM-specific tools for prompting, accuracy measurement, and serving on a variety of runtimes (Huggingface, onnxruntime-genai) and hardware (CPU, GPU, and NPU).
+
 ## Getting Started
 
+### Quick Start
+
 The easiest way to get started is:
 1. `pip install turnkeyml`
 2. Copy a PyTorch example of a model, like the one on this [Huggingface BERT model card](https://huggingface.co/google-bert/bert-base-uncased), into a file named `bert.py`.
@@ -21,6 +25,10 @@ output = model(**encoded_input)
 ```
 3. `turnkey -i bert.py discover export-pytorch`: make a BERT ONNX file from this `bert.py` example.
 
+### LLMs
+
+For LLM setup instructions, see [`turnkey-llm`](https://github.com/onnx/turnkeyml/tree/main/src/turnkeyml/llm).
+
 ## Demo
 
 Here's `turnkey` in action: BERT-Base is exported from PyTorch to ONNX using `torch.onnx.export`, optimized for inference with `onnxruntime`, and converted to fp16 with `onnxmltools`: