metadata
license: agpl-3.0
datasets:
- ssbuild/alaca_chain-of-thought
language:
- en
pipeline_tag: text-generation
Model Card for Model ID
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
Model Description
- Developed by: Shourya Shashank
- 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-2B-4bit is a compact model designed for efficiency, but it comes with certain limitations:
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.
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.
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, ReasonGPT-2B-4bit 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 = "ReasonGPT-2B-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-2B-4bit model and use it to generate an explanation for a given query, keeping in mind the limitations mentioned above.