Update README.md

README.md

@@ -51,7 +51,7 @@ These are experimental first AWQs for the brand-new model format, Mistral.
 
 As of September 29th 2023, they are supported by AutoAWQ, and vLLM (version 0.2).
 
-To use from AutoAW£Q requires installing both AutoAWQ and Transformers from Github. More details are below.
+To use from AutoAWQ requires installing both AutoAWQ and Transformers from Github. More details are below.
 
 <!-- description end -->
 <!-- repositories-available start -->
@@ -86,6 +86,44 @@ Models are released as sharded safetensors files.
 
 <!-- README_AWQ.md-provided-files end -->
 
+<!-- README_AWQ.md-use-from-vllm start -->
+## Serving this model from vLLM
+
+Make sure you are using vLLM version 0.2.
+
+Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
+
+- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
+
+```shell
+python3 python -m vllm.entrypoints.api_server --model TheBloke/Mistral-7B-Instruct-v0.1-AWQ --quantization awq --dtype float16
+```
+
+When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+llm = LLM(model="TheBloke/Mistral-7B-Instruct-v0.1-AWQ", quantization="awq", dtype="float16")
+
+outputs = llm.generate(prompts, sampling_params)
+
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+<!-- README_AWQ.md-use-from-vllm start -->
+
 <!-- README_AWQ.md-use-from-python start -->
 ## How to use this AWQ model from Python code