Update README.md

README.md

@@ -38,7 +38,7 @@ import torch
 from transformers import pipeline
 
 generate_text = pipeline(
-    model="Stevross/Astrid-1B",
+    model="PAIXAI/Astrid-1B",
     torch_dtype="auto",
     trust_remote_code=True,
     use_fast=True,
@@ -76,13 +76,13 @@ from h2oai_pipeline import H2OTextGenerationPipeline
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained(
-    "Stevross/Astrid-1B",
+    "PAIXAI/Astrid-1B",
     use_fast=True,
     padding_side="left",
     trust_remote_code=True,
 )
 model = AutoModelForCausalLM.from_pretrained(
-    "Stevross/Astrid-1B",
+    "PAIXAI/Astrid-1B",
     torch_dtype="auto",
     device_map={"": "cuda:0"},
     trust_remote_code=True,
@@ -108,7 +108,7 @@ You may also construct the pipeline from the loaded model and tokenizer yourself
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "Stevross/Astrid-1B"  # either local folder or huggingface model name
+model_name = "PAIXAI/Astrid-1B"  # either local folder or huggingface model name
 # Important: The prompt needs to be in the same format the model was trained with.
 # You can find an example prompt in the experiment logs.
 prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
@@ -182,7 +182,7 @@ This model was trained using H2O LLM Studio and with the configuration in [cfg.y
 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=Stevross/Astrid-1B --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
+CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=PAIXAI/Astrid-1B --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
 ```