Update README.md

README.md

@@ -13,7 +13,7 @@ thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources
 # Model Card
 ## Summary
 
-This model, Astrid-1B-1, is a GPT-NeoX model for causal language modeling, designed to generate human-like text.
+This model, Astrid-1B-CPU, is a GPT-NeoX model for causal language modeling, designed to generate human-like text.
 It's part of our mission to make AI technology accessible to everyone, focusing on personalization, data privacy, and transparent AI governance. 
 Trained in English, it's a versatile tool for a variety of applications. 
 This model is one of the many models available on our platform, and we currently have a 1B and 7B open-source model.
@@ -37,7 +37,7 @@ import torch
 from transformers import pipeline
 
 generate_text = pipeline(
-    model="Stevross/Astrid-1B-CPU",
+    model="PAIXAI/Astrid-1B-CPU",
     torch_dtype="auto",
     trust_remote_code=True,
     use_fast=True,
@@ -75,13 +75,13 @@ from h2oai_pipeline import H2OTextGenerationPipeline
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained(
-    "Stevross/Astrid-1B-CPU",
+    "PAIXAI/Astrid-1B-CPU",
     use_fast=True,
     padding_side="left",
     trust_remote_code=True,
 )
 model = AutoModelForCausalLM.from_pretrained(
-    "Stevross/Astrid-1B-CPU",
+    "PAIXAI/Astrid-1B-CPU",
     torch_dtype="auto",
     device_map={"": "cuda:0"},
     trust_remote_code=True,
@@ -107,7 +107,7 @@ You may also construct the pipeline from the loaded model and tokenizer yourself
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "Stevross/Astrid-1B-CPU"  # either local folder or huggingface model name
+model_name = "PAIXAI/Astrid-1B-CPU"  # 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|>"
@@ -181,7 +181,7 @@ 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-CPU --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-CPU --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
 ```