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--- |
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license: agpl-3.0 |
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datasets: |
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- ssbuild/alaca_chain-of-thought |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# Model Card for Model ID |
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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. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [Shourya Shashank](https://huggingface.co/shouryashashank) |
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- **Model type:** Transformer-based Language Model |
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- **Language(s) (NLP):** English |
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- **License:** AGPL-3.0 |
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- **Finetuned from model [optional]:** google/gemma-2b |
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## Uses |
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* Problem Explanation: Generate detailed descriptions and reasoning for various problems, useful in educational contexts, customer support, and automated troubleshooting. |
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* Natural Language Understanding: Ideal for applications requiring human-like text understanding and generation, such as chatbots, virtual assistants, and content generation tools. |
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* Compact Deployment: Suitable for environments with limited computational resources due to its small size and 4-bit quantization. |
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### Direct Use |
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* Problem Explanation: Generate detailed descriptions and reasoning for various problems, useful in educational contexts, customer support, and automated troubleshooting. |
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* Natural Language Understanding: Ideal for applications requiring human-like text understanding and generation, such as chatbots, virtual assistants, and content generation tools. |
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* Compact Deployment: Suitable for environments with limited computational resources due to its small size and 4-bit quantization. |
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### Downstream Use [optional] |
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* Educational Tools: Fine-tune the model on educational datasets to provide detailed explanations and reasoning for academic subjects. |
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* Customer Support: Fine-tune on customer service interactions to enhance automated support systems with accurate and context-aware responses. |
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## Bias, Risks, and Limitations |
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### Limitations |
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**ReasonGPT-2B-4bit** is a compact model designed for efficiency, but it comes with certain limitations: |
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1. **Calculation Accuracy**: |
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- 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. |
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2. **Chat Template Support**: |
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- 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. |
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3. **Limited Context Understanding**: |
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- With a smaller parameter size, the model may have limitations in understanding and generating contextually rich and nuanced responses compared to larger models. |
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4. **Bias and Fairness**: |
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- 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. |
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5. **Resource Constraints**: |
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- 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. |
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### Example Usage: |
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```python |
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import predacons |
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# Load the model and tokenizer |
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model_path = "Precacons/ReasonGPT-2B-4bit" |
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model = predacons.load_model(model_path) |
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tokenizer = predacons.load_tokenizer(model_path) |
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# Example usage |
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sequence = "Explain the concept of acceleration in physics." |
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output,tokenizer =predacons.generate(model = model, |
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sequence = sequence, |
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max_length = 500, |
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tokenizer = tokenizer, |
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trust_remote_code = True) |
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# Decode and print the generated text |
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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print(generated_text) |
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``` |
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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. |
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## Model Card Authors [optional] |
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[Shourya Shashank](https://huggingface.co/shouryashashank) |
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