Update README.md

README.md

@@ -9,4 +9,100 @@ tags:
 - 'reasoning '
 - chain of thought
 - problem solving
----
+---
+## Model Details
+
+### Model Description
+
+Precacons/Pico-Lamma-3.2-1B-Reasoning-Instruct
+Model Overview: Precacons/Pico-Lamma-3.2-1B-Reasoning-Instruct is a highly efficient and accurate language model fine-tuned on the “meta-llama/Llama-3.2-1B-Instruct” base model. Despite its compact size of just 0.99GB, it delivers exceptional performance, particularly in tasks requiring logical reasoning and structured thought processes.
+
+
+- **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]:** meta-llama/Llama-3.2-1B-Instruct
+
+
+#### Key Features:
+
+* **Compact Size**: At only 0.99GB, it is lightweight and easy to deploy, making it suitable for environments with limited computational resources.
+* **High Accuracy**: The model’s training on a specialized chain of thought and reasoning dataset enhances its ability to perform complex reasoning tasks with high precision.
+* **Fine-Tuned on Meta-Llama**: Leveraging the robust foundation of the “meta-llama/Llama-3.2-1B-Instruct” model, it inherits strong language understanding and generation capabilities.
+
+#### Applications:
+
+* **Educational Tools**: Ideal for developing intelligent tutoring systems that require nuanced understanding and explanation of concepts.
+* **Customer Support**: Enhances automated customer service systems by providing accurate and contextually relevant responses.
+* **Research Assistance**: Assists researchers in generating hypotheses, summarizing findings, and exploring complex datasets.
+
+
+
+## Uses
+* Lightweight: The software is designed to be extremely lightweight, ensuring it can run efficiently on any system without requiring extensive resources.
+* Natural Language Understanding: Ideal for applications requiring human-like text understanding and generation, such as chatbots, virtual assistants, and content generation tools.
+* Small Size: Despite its compact size of just 0.99GB, it packs a powerful punch, making it easy to download and install.
+* High Reliability: The reliability is significantly enhanced due to the chain-of-thought approach integrated into its design, ensuring consistent and accurate performance.
+### 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
+
+**Pico-Lamma-3.2-1B-Reasoning-Instruct** is a compact model designed for efficiency, but it comes with certain limitations:
+
+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, Pico-Lamma-3.2-1B-Reasoning-Instruct 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 = "Precacons/Pico-Lamma-3.2-1B-Reasoning-Instruct"
+model = predacons.load_model(model_path) 
+tokenizer = predacons.load_tokenizer(model_path)
+
+# Example usage
+chat = [
+    {"role": "user", "content": "A train travelling at a speed of 60 km/hr is stopped in 15 seconds by applying the brakes. Determine its retardation."},
+]
+res = predacons.chat_generate(model = p_model,
+        sequence = chat,
+        max_length = 5000,
+        tokenizer = p_tokenizer,
+        trust_remote_code = True,
+        do_sample=True,
+                
+        )
+
+print(res)
+```
+
+This example demonstrates how to load the `Pico-Lamma-3.2-1B-Reasoning-Instruct` 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)