---
license: agpl-3.0
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
library_name: predacons
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)