Update README.md

README.md

@@ -13,11 +13,11 @@ license: apache-2.0
 library_name: transformers
 ---
 
-# Laxo Pro
+# Loxa Pro
 
 ## Model Description
 
-Laxo Pro is a high-quality Large Language Model (LLM) designed to excel in a wide range of tasks, including:
+Loxa Pro is a high-quality Large Language Model (LLM) designed to excel in a wide range of tasks, including:
 
 *   **Mathematics:** Solving mathematical problems, performing calculations, and providing mathematical explanations.
 *   **Coding:** Generating code snippets, debugging code, and answering programming-related questions.
@@ -25,27 +25,27 @@ Laxo Pro is a high-quality Large Language Model (LLM) designed to excel in a wid
 *   **Science Questions:** Providing information and explanations on various scientific topics.
 *   **Daily Tasks:** Assisting with everyday tasks, such as writing emails, setting reminders, generating to-do lists, and more.
 
-Laxo Pro employs the **CA (Combine Architectures)** method, which enables it to effectively address diverse queries and tasks. This model surpasses its predecessors, Loxa-4B and Loxa-3B, in terms of accuracy and performance.
+Loxa Pro employs the **CA (Combine Architectures)** method, which enables it to effectively address diverse queries and tasks. This model surpasses its predecessors, Loxa-4B and Loxa-3B, in terms of accuracy and performance.
 
 ## Key Features
 
-*   **High Accuracy:** Laxo Pro demonstrates superior accuracy compared to Loxa-4B and Loxa-3B, providing more reliable and precise responses.
-*   **Broad Capabilities:**  Handles a diverse range of tasks, from complex mathematical problems and coding challenges to general knowledge and everyday tasks.
+*   **High Accuracy:** Loxa Pro demonstrates superior accuracy compared to Loxa-4B and Loxa-3B, providing more reliable and precise responses.
+*   **Broad Capabilities:** Handles a diverse range of tasks, from complex mathematical problems and coding challenges to general knowledge and everyday tasks.
 *   **Optimized for Efficiency:** The model is well-optimized to run efficiently even on smaller GPUs, making it accessible for users with limited computational resources.
 *   **CA (Combine Architectures) Method:** Leverages the CA method to effectively combine different architectural strengths, enhancing overall performance.
 
 ## Intended Use
 
-Laxo Pro is intended for a wide range of applications, including:
+Loxa Pro is intended for a wide range of applications, including:
 
-*   **Research:**  As a tool for research in natural language processing, artificial intelligence, and related fields.
+*   **Research:** As a tool for research in natural language processing, artificial intelligence, and related fields.
 *   **Education:** As an educational aid for students and educators in various subjects.
 *   **Development:** As a component in building intelligent applications, chatbots, and virtual assistants.
 *   **Personal Use:** As a versatile tool to assist with daily tasks, answer questions, and provide information.
 
 ## Limitations
 
-*   **Potential Biases:** Like all LLMs, Laxo Pro may reflect biases present in its training data.
+*   **Potential Biases:** Like all LLMs, Loxa Pro may reflect biases present in its training data.
 *   **Factual Accuracy:** While highly accurate, the model may occasionally generate incorrect or misleading information. It is always recommended to verify information from multiple sources.
 *   **Resource Requirements:** Although optimized, the model still requires a certain level of computational resources to run effectively.
 
@@ -57,9 +57,8 @@ Laxo Pro is intended for a wide range of applications, including:
 from transformers import pipeline
 
 messages = [
-    {"role": "user", "content": "You are alex, a helpful assistant"},
-    {"role": "user", "content": "Who are you?"},
+    {"role": "user", "content": "Write softmax formula in math style for me"},
 ]
-pipe = pipeline("text-generation", model="explorewithai/LaxoPro", device_map = "cuda")
+pipe = pipeline("text-generation", model="explorewithai/LoxaPro", device_map = "cuda")
 pipe(messages)
 ```