Update README.md

README.md

@@ -5,7 +5,7 @@ datasets:
 
 ## Model Details
 
-This model is an int4 model with group_size 128 and symmetric quantization of [falcon-three-7b]() generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision `a10e358` to use AutoGPTQ format, with revision `e9aa317` to use AutoAWQ format
+This model is an int4 model with group_size 128 and symmetric quantization of [Falcon3-7B-Base](https://huggingface.co/tiiuae/Falcon3-7B-Base) generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision `a10e358` to use AutoGPTQ format, with revision `e9aa317` to use AutoAWQ format
 
 ## How To Use
 ### INT4 Inference(CPU/HPU/CUDA)
@@ -13,7 +13,7 @@ This model is an int4 model with group_size 128 and symmetric quantization of [f
 from auto_round import AutoRoundConfig  ##must import for auto_round format
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-quantized_model_dir = "OPEA/falcon-three-7b-int4-sym-inc"
+quantized_model_dir = "OPEA/Falcon3-7B-Base-int4-sym-inc"
 tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir)
 model = AutoModelForCausalLM.from_pretrained(
     quantized_model_dir,
@@ -72,7 +72,7 @@ text = "There is a girl who likes adventure,"
 pip3 install lm-eval==0.4.5
 
 ```bash
-auto-round --model "OPEA/falcon-three-7b-int4-sym-inc" --eval --eval_bs 16  --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu
+auto-round --model "OPEA/Falcon3-7B-Base-int4-sym-inc" --eval --eval_bs 16  --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu
 ```
 
 | Metric                      |  BF16  |  INT4  |
@@ -94,7 +94,7 @@ auto-round --model "OPEA/falcon-three-7b-int4-sym-inc" --eval --eval_bs 16  --ta
 Here is the sample command to generate the model. 
 ```bash
 auto-round  \
---model falcon-three-7b \
+--model tiiuae/Falcon3-7B-Base \
 --device 0 \
 --group_size 128 \
 --bits 4 \