Added vllm run commands

Added an example of using vllm in a basic configuration as well as the advanced configuration with ngram spec dec.

README.md

@@ -22,6 +22,24 @@ The model has been fine-tuned using the [zeta dataset](https://huggingface.co/da
 The dataset used for training is available at:
 [zed-industries/zeta](https://huggingface.co/datasets/zed-industries/zeta)
 
+## Running Zeta
+
+### vLLM - Simple
+
+`vllm serve zed-industries/zeta --served-model-name zeta`
+
+### vLLM - Advanced
+
+- [Quantization](https://docs.vllm.ai/en/latest/features/quantization/fp8.html#) vLLM supports FP8 (8-bit floating point) weight and activation quantization using hardware acceleration on GPUs such as Nvidia H100 and AMD MI300x.
+
+- [NGram Speculative Decoding](https://docs.vllm.ai/en/latest/features/spec_decode.html#speculating-by-matching-n-grams-in-the-prompt) configures vLLM to use
+speculative decoding where proposals are generated by matching n-grams in the prompt. This is a great fit for edit predictions since many of the tokens are already present in the prompt and
+the model is only needed to generate changes to the code file.
+
+
+`vllm serve zed-industries/zeta --served-model-name zeta --enable-prefix-caching --enable-chunked-prefill --quantization="fp8" --speculative-model [ngram] --ngram-prompt-lookup-max 4 --ngram-prompt-lookup-min 2 --num-speculative-tokens 8`
+
+
 ## Learn More
 
 For more insights about the model and its integration in Zed, check out the official blog post: