Update models/README.md

Co-authored-by: shrekris-anyscale <[email protected]>
Signed-off-by: Sihan Wang <[email protected]>

models/README.md

@@ -51,7 +51,7 @@ RayLLM supports continuous batching, meaning incoming requests are processed as
 * `scheduler_policy` is to choose scheduler policy between max_utilization/guaranteed_no_evict.
 (`MAX_UTILIZATION` packs as many requests as the underlying TRT engine can support in any iteration of the InflightBatching generation loop. While this is expected to maximize GPU throughput, it might require that some requests be paused and restarted depending on peak KV cache memory availability.
 `GUARANTEED_NO_EVICT` uses KV cache more conservatively and guarantees that a request, once started, runs to completion without eviction.)
-* `logger_level` is to configure log level for TensorRT-LLM engine. ("INFO", "ERROR", "VERBOSE", "WARNING")
+* `logger_level` is to configure log level for TensorRT-LLM engine. ("VERBOSE", "INFO", "WARNING", "ERROR")
 * `max_num_sequences` is the maximum number of requests/sequences the backend can maintain state
 * `max_tokens_in_paged_kv_cache` is to configure the maximum number of tokens in the paged kv cache.
 * `kv_cache_free_gpu_mem_fraction` is to configure K-V Cache free gpu memory fraction.