diff --git a/examples/text-generation/README.md b/examples/text-generation/README.md
index 4257827596..8aaccfd124 100755
--- a/examples/text-generation/README.md
+++ b/examples/text-generation/README.md
@@ -282,7 +282,7 @@ You will also need to add `--torch_compile` and `--parallel_strategy="tp"` in yo
 Here is an example:
 ```bash
 PT_ENABLE_INT64_SUPPORT=1 PT_HPU_LAZY_MODE=0 python ../gaudi_spawn.py  --world_size 8 run_generation.py \
---model_name_or_path meta-llama/Llama-2-70b-hf  \
+--model_name_or_path meta-llama/Llama-2-7b-hf  \
 --trim_logits \
 --use_kv_cache \
 --attn_softmax_bf16 \
@@ -593,6 +593,10 @@ For more details see [documentation](https://docs.habana.ai/en/latest/PyTorch/Mo
 Llama2-7b in UINT4 weight only quantization is enabled using [AutoGPTQ Fork](https://github.com/HabanaAI/AutoGPTQ), which provides quantization capabilities in PyTorch.
 Currently, the support is for UINT4 inference of pre-quantized models only.
 
+```bash
+BUILD_CUDA_EXT=0 python -m pip install -vvv --no-build-isolation git+https://github.com/HabanaAI/AutoGPTQ.git
+```
+
 You can run a *UINT4 weight quantized* model using AutoGPTQ by setting the following environment variables:
 `SRAM_SLICER_SHARED_MME_INPUT_EXPANSION_ENABLED=false ENABLE_EXPERIMENTAL_FLAGS=true` before running the command,
 and by adding the argument `--load_quantized_model_with_autogptq`.