license: mit
datasets:
- fka/awesome-chatgpt-prompts
- gopipasala/fka-awesome-chatgpt-prompts
metrics:
- character
base_model:
- meta-llama/Llama-3.2-11B-Vision-Instruct
new_version: meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: summarization
library_name: diffusers
tags:
- legal
- text-generation-inference
- transformers
- rust
- inference-endpoint
Model Overview Section:
Add a brief paragraph summarizing the model’s purpose, what makes it unique, and its intended users.
For example: vbnet Copy code This model, developed by Rithu Paran, is designed to provide high-quality text summarization, making it ideal for applications in content curation, news summarization, and document analysis. Leveraging the Meta-Llama architecture, it delivers accurate, concise summaries while maintaining key information, and is optimized for general-purpose use.
Model Description: Under Model Type, clarify the model's focus on general text summarization or a specific summarization task (e.g., long-form content, news). Update Language(s) with more detail on the model's primary language capabilities.
Model Use Cases: Expand Direct Use and Out-of-Scope Use with specific examples to guide users. Direct Use: News article summarization, summarizing reports for quick insights, content summarization for educational purposes. Out-of-Scope Use: Avoid using it for legal or medical content without specialized training.
Bias, Risks, and Limitations: Include any known biases related to the datasets used. For example, “The model may reflect certain cultural or societal biases present in the training data.” Add a note on limitations in terms of accuracy for complex technical summaries or if the model occasionally generates nonsensical summaries.
How to Get Started with the Model: Add more usage tips, such as how to adjust parameters for different summary lengths.
Example: python Copy code summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
Training Details: In Training Hyperparameters, provide a rationale for the chosen batch size and learning rate. If you have insights into why AdamW was chosen as the optimizer, it would be helpful to include that too.
Environmental Impact: Add a short sentence on the steps taken to minimize the environmental impact, if applicable.
Evaluation: If possible, include the exact ROUGE and BLEU scores to show the model’s summarization performance.
Additional Information: You could add a Future Work or Planned Improvements section if you plan to enhance the model further. In the Contact section, you might mention if you are open to feedback, bug reports, or contributions. Here’s a short sample revision for the Model Details section:
Model Details Model Description This model by Rithu Paran focuses on text summarization, reducing lengthy content into concise summaries. Built on the Meta-Llama architecture, it has been finetuned to effectively capture key points from general text sources.
Purpose: General-purpose text summarization Developer: Rithu Paran Architecture: Transformer-based Llama-3 Language: Primarily English Model Versions
Base Model: Meta-Llama/Llama-3.2-11B-Vision-Instruct Current Finetuned Model: Meta-Llama/Llama-3.1-8B-Instruct For the full model card, keep these ideas in mind and feel free to customize it further to fit your style! Let me know if you’d like more specific revisions.