Update README.md

README.md

@@ -6,15 +6,22 @@ tags:
 - gpt
 - llm
 - large language model
-- h2o-llmstudio
-inference: false
-thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
+- PAIX.Cloud
+inference: true
+thumbnail: https://static.wixstatic.com/media/bdee4e_8aa5cefc86024bc88f7e20e3e19d9ff3~mv2.png/v1/fill/w_192%2Ch_192%2Clg_1%2Cusm_0.66_1.00_0.01/bdee4e_8aa5cefc86024bc88f7e20e3e19d9ff3~mv2.png
 ---
 # Model Card
 ## Summary
 
-This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
-- Base model: [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
+- Base model: [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+
+This model, Astrid-7B-Assistant is a Mistral-7B base 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.
+
+This model was trained by [PAIX.Cloud](https://www.paix.cloud/).
+- Wait list: [Wait List](https://www.paix.cloud/join-waitlist)
 
 
 ## Usage
@@ -37,7 +44,7 @@ Also make sure you are providing your huggingface token to the pipeline if the m
 from transformers import pipeline
 
 generate_text = pipeline(
-    model="Stevross/Astrid-7B-OpenOrce-3",
+    model="PAIXAI/Astrid-Mistral-7B,
     torch_dtype="auto",
     trust_remote_code=True,
     use_fast=True,
@@ -75,13 +82,13 @@ from h2oai_pipeline import H2OTextGenerationPipeline
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained(
-    "Stevross/Astrid-7B-OpenOrce-3",
+    "PAIXAI/Astrid-Mistral-7B,
     use_fast=True,
     padding_side="left",
     trust_remote_code=True,
 )
 model = AutoModelForCausalLM.from_pretrained(
-    "Stevross/Astrid-7B-OpenOrce-3",
+    "PAIXAI/Astrid-Mistral-7B,
     torch_dtype="auto",
     device_map={"": "cuda:0"},
     trust_remote_code=True,
@@ -107,7 +114,7 @@ You may also construct the pipeline from the loaded model and tokenizer yourself
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "Stevross/Astrid-7B-OpenOrce-3"  # either local folder or huggingface model name
+model_name = "PAIXAI/Astrid-Mistral-7B  # 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?<|im_end|><|answer|>"