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comps/llms/text-generation/vllm/langchain/README.md

@@ -146,19 +146,17 @@ bash launch_vllm_service_openvino.sh -d gpu
 ```
 
 #### Performance tips
-----------------
 
-vLLM OpenVINO backend environment variables
+---
 
+vLLM OpenVINO backend environment variables
 
-- `VLLM_OPENVINO_DEVICE` to specify which device utilize for the inference. If there are multiple GPUs in the system, additional indexes can be used to choose the proper one (e.g, ``VLLM_OPENVINO_DEVICE=GPU.1``). If the value is not specified, CPU device is used by default.
+- `VLLM_OPENVINO_DEVICE` to specify which device utilize for the inference. If there are multiple GPUs in the system, additional indexes can be used to choose the proper one (e.g, `VLLM_OPENVINO_DEVICE=GPU.1`). If the value is not specified, CPU device is used by default.
 
 - `VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON` to enable U8 weights compression during model loading stage. By default, compression is turned off. You can also export model with different compression techniques using `optimum-cli` and pass exported folder as `<model_id>`
 
-
 ##### CPU performance tips
 
-
 vLLM OpenVINO backend uses the following environment variables to control behavior:
 
 - `VLLM_OPENVINO_KVCACHE_SPACE` to specify the KV Cache size (e.g, `VLLM_OPENVINO_KVCACHE_SPACE=40` means 40 GB space for KV cache), larger setting will allow vLLM running more requests in parallel. This parameter should be set based on the hardware configuration and memory management pattern of users.
@@ -176,7 +174,7 @@ OpenVINO best known configuration is:
 
 ##### GPU performance tips
 
-GPU device implements the logic for automatic detection of available GPU memory and, by default, tries to reserve as much memory as possible for the KV cache (taking into account ``gpu_memory_utilization`` option). However, this behavior can be overridden by explicitly specifying the desired amount of memory for the KV cache using ``VLLM_OPENVINO_KVCACHE_SPACE`` environment variable (e.g, ``VLLM_OPENVINO_KVCACHE_SPACE=8`` means 8 GB space for KV cache).
+GPU device implements the logic for automatic detection of available GPU memory and, by default, tries to reserve as much memory as possible for the KV cache (taking into account `gpu_memory_utilization` option). However, this behavior can be overridden by explicitly specifying the desired amount of memory for the KV cache using `VLLM_OPENVINO_KVCACHE_SPACE` environment variable (e.g, `VLLM_OPENVINO_KVCACHE_SPACE=8` means 8 GB space for KV cache).
 
 Currently, the best performance using GPU can be achieved with the default vLLM execution parameters for models with quantized weights (8 and 4-bit integer data types are supported) and `preemption-mode=swap`.
 
@@ -185,7 +183,6 @@ OpenVINO best known configuration for GPU is:
     $ VLLM_OPENVINO_DEVICE=GPU VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON \
         python3 vllm/benchmarks/benchmark_throughput.py --model meta-llama/Llama-2-7b-chat-hf --dataset vllm/benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json
 
-
 ### 2.4 Query the service
 
 And then you can make requests like below to check the service status: