Update README.md

README.md

@@ -8,7 +8,7 @@ pipeline_tag: text-generation
 ---
 # Model Card for Model ID
 
-The model `Precacons/ReasonGPT-2.5B-4bit` is a lightweight language model based on the GEMMA architecture. It is designed to provide reasoning and explanations for any given problem. Despite its powerful capabilities, it is very compact, with a size of just 2.16 GB, making it efficient for deployment and use in various applications.
+The model `Precacons/ReasonGPT-2B-4bit` is a lightweight language model based on the GEMMA architecture. It is designed to provide reasoning and explanations for any given problem. Despite its powerful capabilities, it is very compact, with a size of just 2.16 GB, making it efficient for deployment and use in various applications.
 
 
 ## Model Details
@@ -47,7 +47,7 @@ The model `Precacons/ReasonGPT-2.5B-4bit` is a lightweight language model based
 
 ### Limitations
 
-**ReasonGPT-2.5B-4bit** is a compact model designed for efficiency, but it comes with certain limitations:
+**ReasonGPT-2B-4bit** is a compact model designed for efficiency, but it comes with certain limitations:
 
 1. **Calculation Accuracy**:
    - Due to its small size, the model may not perform complex calculations with high accuracy. It is optimized for reasoning and explanations rather than precise numerical computations.
@@ -59,7 +59,7 @@ The model `Precacons/ReasonGPT-2.5B-4bit` is a lightweight language model based
    - With a smaller parameter size, the model may have limitations in understanding and generating contextually rich and nuanced responses compared to larger models.
 
 4. **Bias and Fairness**:
-   - Like all language models, ReasonGPT-2.5B-4bit may exhibit biases present in the training data. Users should be cautious of potential biases in the generated outputs.
+   - Like all language models, ReasonGPT-2B-4bit may exhibit biases present in the training data. Users should be cautious of potential biases in the generated outputs.
 
 5. **Resource Constraints**:
    - While the model is designed to be efficient, it still requires a GPU for optimal performance. Users with limited computational resources may experience slower inference times.
@@ -70,7 +70,7 @@ The model `Precacons/ReasonGPT-2.5B-4bit` is a lightweight language model based
 import predacons
 
 # Load the model and tokenizer
-model_path = "ReasonGPT-2.5B-4bit"
+model_path = "ReasonGPT-2B-4bit"
 model = predacons.load_model(model_path = model_path) 
 tokenizer = predacons.load_tokenizer(model_path)
 
@@ -87,7 +87,7 @@ generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(generated_text)
 ```
 
-This example demonstrates how to load the `ReasonGPT-2.5B-4bit` model and use it to generate an explanation for a given query, keeping in mind the limitations mentioned above.
+This example demonstrates how to load the `ReasonGPT-2B-4bit` model and use it to generate an explanation for a given query, keeping in mind the limitations mentioned above.