Update README.md

README.md

@@ -5,4 +5,93 @@ datasets:
 language:
 - en
 pipeline_tag: text-generation
----
+---
+# Model Card for Model ID
+
+The model `google/gemma-2b` 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
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [Shourya Shashank](https://huggingface.co/shouryashashank)
+- **Model type:** Transformer-based Language Model
+- **Language(s) (NLP):** English
+- **License:** AGPL-3.0
+- **Finetuned from model [optional]:** google/gemma-2b
+
+## Uses
+* Problem Explanation: Generate detailed descriptions and reasoning for various problems, useful in educational contexts, customer support, and automated troubleshooting.
+* Natural Language Understanding: Ideal for applications requiring human-like text understanding and generation, such as chatbots, virtual assistants, and content generation tools.
+* Compact Deployment: Suitable for environments with limited computational resources due to its small size and 4-bit quantization.
+### Direct Use
+
+* Problem Explanation: Generate detailed descriptions and reasoning for various problems, useful in educational contexts, customer support, and automated troubleshooting.
+* Natural Language Understanding: Ideal for applications requiring human-like text understanding and generation, such as chatbots, virtual assistants, and content generation tools.
+* Compact Deployment: Suitable for environments with limited computational resources due to its small size and 4-bit quantization.
+
+
+### Downstream Use [optional]
+
+* Educational Tools: Fine-tune the model on educational datasets to provide detailed explanations and reasoning for academic subjects.
+* Customer Support: Fine-tune on customer service interactions to enhance automated support systems with accurate and context-aware responses.
+
+
+
+## Bias, Risks, and Limitations
+
+### Limitations
+
+**ReasonGPT-2.5B-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.
+
+2. **Chat Template Support**:
+   - The model does not support chat templates because of the format of the training dataset. It may not handle conversational contexts as effectively as models specifically trained for chat applications.
+
+3. **Limited Context Understanding**:
+   - 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.
+
+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.
+
+
+### Example Usage:
+```python
+import predacons
+
+# Load the model and tokenizer
+model_path = "ReasonGPT-2.5B-4bit"
+model = predacons.load_model(model_path = model_path) 
+tokenizer = predacons.load_tokenizer(model_path)
+
+# Example usage
+sequence = "Explain the concept of acceleration in physics."
+output,tokenizer =predacons.generate(model = model,
+        sequence = sequence,
+        max_length = 50,
+        tokenizer = tokenizer,
+        trust_remote_code = True)
+
+# Decode and print the generated text
+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.
+
+
+
+## Model Card Authors [optional]
+
+[Shourya Shashank](https://huggingface.co/shouryashashank)
+