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--- |
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license: mit |
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datasets: |
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- fka/awesome-chatgpt-prompts |
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- gopipasala/fka-awesome-chatgpt-prompts |
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metrics: |
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- character |
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base_model: |
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- meta-llama/Llama-3.2-11B-Vision-Instruct |
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new_version: meta-llama/Llama-3.1-8B-Instruct |
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pipeline_tag: summarization |
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library_name: diffusers |
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tags: |
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- legal |
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- text-generation-inference |
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- transformers |
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- rust |
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- inference-endpoint |
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--- |
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Model Overview Section: |
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Add a brief paragraph summarizing the model’s purpose, what makes it unique, and its intended users. |
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For example: |
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vbnet |
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Copy code |
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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. |
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2. Model Description: |
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Under Model Type, clarify the model's focus on general text summarization or a specific summarization task (e.g., long-form content, news). |
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Update Language(s) with more detail on the model's primary language capabilities. |
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4. Model Use Cases: |
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Expand Direct Use and Out-of-Scope Use with specific examples to guide users. |
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Direct Use: News article summarization, summarizing reports for quick insights, content summarization for educational purposes. |
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Out-of-Scope Use: Avoid using it for legal or medical content without specialized training. |
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6. Bias, Risks, and Limitations: |
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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.” |
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Add a note on limitations in terms of accuracy for complex technical summaries or if the model occasionally generates nonsensical summaries. |
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8. How to Get Started with the Model: |
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Add more usage tips, such as how to adjust parameters for different summary lengths. |
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Example: |
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python |
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Copy code |
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summary = summarizer(text, max_length=150, min_length=50, do_sample=False) |
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10. Training Details: |
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In Training Hyperparameters, provide a rationale for the chosen batch size and learning rate. |
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If you have insights into why AdamW was chosen as the optimizer, it would be helpful to include that too. |
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12. Environmental Impact: |
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Add a short sentence on the steps taken to minimize the environmental impact, if applicable. |
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14. Evaluation: |
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If possible, include the exact ROUGE and BLEU scores to show the model’s summarization performance. |
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15. Additional Information: |
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You could add a Future Work or Planned Improvements section if you plan to enhance the model further. |
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In the Contact section, you might mention if you are open to feedback, bug reports, or contributions. |
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Here’s a short sample revision for the Model Details section: |
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Model Details |
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Model Description |
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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. |
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Purpose: General-purpose text summarization |
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Developer: Rithu Paran |
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Architecture: Transformer-based Llama-3 |
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Language: Primarily English |
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Model Versions |
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Base Model: Meta-Llama/Llama-3.2-11B-Vision-Instruct |
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Current Finetuned Model: Meta-Llama/Llama-3.1-8B-Instruct |
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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. |
