ALLaM-7B Model Card
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Model Details
Model Description
Model Name: ALLaM-7B
Model Type: Language Model
Model Size: 7 billion parameters
Developed and funded by: Saudi Authority for Data and Artificial Intelligence
Language(s) (NLP): Arabic
Task(s): Text Generation, Text Classification, Text Summarization, Question Answering
Architecture: [More Information Needed]
License: [More Information Needed]
Training Data: [List the datasets used for training]
Training Procedure: [Briefly describe the training methodology, hardware, and any special techniques like fine-tuning on specific tasks.]
Finetuned from model [optional]: [More Information Needed]
Input Format: Text (string of characters)
Output Format: Text (generated or classified text)
Maximum Token Length: [Token limit, e.g., 1024 tokens]
Pre-training Data: [Mention any corpora or datasets used during pre-training]
Fine-tuning: [Indicate if the model is fine-tuned for specific tasks]
Intended Use: ALLaM-7B is designed for a wide range of natural language processing (NLP) tasks, such as:
- Text generation
- Summarization
- Question answering
- Language modeling
- Text classification
- [Other tasks based on the model's capabilities]
Examples of Use Cases:
- Conversational AI
- Content creation tools
- Automatic summarization tools
- Question answering systems
- Sentiment analysis
- [Include any other relevant use cases]
Performance: • Benchmarking: [Provide performance metrics on popular NLP benchmarks] • Accuracy: [List any accuracy results for downstream tasks] • Inference Speed: [Include any details on inference latency and speed]
Limitations: • Bias and Fairness: As with many large-scale models, ALLaM-7B may exhibit biases present in the training data.
• Generalization: The model may not generalize well on highly domain-specific tasks without further fine-tuning.
• Complexity: Due to its size (7 billion parameters), the model requires substantial computational resources for inference and fine-tuning.
Ethical Considerations: • Potential for Misuse: Like other large language models, ALLaM-7B could be used to generate harmful, misleading, or biased content if not monitored properly.
• Biases: The model could reflect and perpetuate harmful stereotypes or biases present in the training data. Users should take care when deploying it in sensitive applications.
Acknowledgments: • This model is based on Transformer Architecture and was trained on large-scale datasets like [Dataset Name(s)].
• Special thanks to the [SDAIA ALLaM Research Lab] for their work in developing this model.
Citation: If you use ALLaM-7B in your work, please cite the following: scss Copy code @inproceedings{Allam2025, title={ALLaM-7B: A 7 Billion Parameter Transformer for General NLP Tasks}, author={SDAIA ALLaM Research Lab}, year={2025}, booktitle={Proceedings of the NLP Conference}, }
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License: • License: CC-BY-SA-4.0 • Model Availability: Available for research and commercial use under the terms of the CC-BY-SA-4.0 license. Please ensure attribution and share alike when redistributing or modifying the model.
Model Sources:
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Demo [optional]: [More Information Needed]
How to Use:
Install the Required Libraries:: bash Copy code pip install transformers
Uses
Direct Use
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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#### Factors
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#### Metrics
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### Results
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Model Examination [optional]
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