metadata
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
- 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:
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.
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.
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:
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 = model,
sequence = chat,
max_length = 5000,
tokenizer = 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.