Update README.md

README.md

@@ -34,7 +34,7 @@ This optimization reduces the number of bits per parameter from 16 to 8, reducin
 Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations.
 [AutoFP8](https://github.com/neuralmagic/AutoFP8) is used for quantization with 512 sequences of UltraChat.
 
-## Deployment
+<!-- ## Deployment
 
 ### Use with vLLM
 
@@ -65,12 +65,13 @@ generated_text = outputs[0].outputs[0].text
 print(generated_text)
 ```
 
-vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. -->
 
 ## Creation
 
 This model was created by applying [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py), as presented in the code snipet below.
 Although AutoFP8 was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoFP8.
+Note that ```transformers``` must be built from source.
 
 ```python
 from datasets import load_dataset
@@ -105,6 +106,7 @@ model.save_quantized(quantized_model_dir)
 ## Evaluation
 
 The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+Note that ```vllm``` must be built from source.
 ```
 lm_eval \
   --model vllm \