llava model added to readme gaudi

README_GAUDI.md

@@ -141,6 +141,7 @@ The following configurations have been validated to be function with Gaudi2 devi
 - [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
 - [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on single HPU or with tensor parallelism on 2x HPU, BF16 datatype with random or greedy sampling
 - [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) with tensor parallelism on 2x HPU, BF16 datatype with random or greedy sampling
+- [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on single HPU or with tensor parallelism on 8x HPU, BF16 datatype
 
 # Performance Tuning
 
@@ -368,6 +369,7 @@ Additionally, there are HPU PyTorch Bridge environment variables impacting vLLM
 
 - `PT_HPU_LAZY_MODE`: if `0`, PyTorch Eager backend for Gaudi will be used, if `1` PyTorch Lazy backend for Gaudi will be used. `1` is the default.
 - `PT_HPU_ENABLE_LAZY_COLLECTIVES` must be set to `true` for tensor parallel inference with HPU Graphs.
+- `PT_HPUGRAPH_DISABLE_TENSOR_CACHE` must be set to `false` for llava model.
 
 # Quantization, FP8 Inference and Model Calibration Process