Update README.md

README.md

@@ -27,6 +27,7 @@ To use the model with the `transformers` library on a machine with GPUs, first m
 
 ```bash
 pip install transformers==4.29.2
+pip install bitsandbytes==0.39.0
 pip install accelerate==0.19.0
 pip install torch==2.0.0
 pip install einops==0.6.1
@@ -36,12 +37,13 @@ pip install einops==0.6.1
 import torch
 from transformers import pipeline, BitsAndBytesConfig, AutoTokenizer
 
+model_kwargs = {}
+
+# optional quantization
 quantization_config = BitsAndBytesConfig(
     load_in_8bit=True,
     llm_int8_threshold=3.0,
 )
-
-model_kwargs = {}
 model_kwargs["quantization_config"] = quantization_config
 
 tokenizer = AutoTokenizer.from_pretrained(
@@ -183,16 +185,6 @@ RWForCausalLM(
 
 This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
 
-
-## Model Validation
-
-Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=psinger/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
-```
-
-
 ## Disclaimer
 
 Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.