Model Card for sar-i-65b

Model Details

• Model Name: sar-i-65b
• Version: 1.2
• Developed by: BushAI

Intended Use

• Primary Use Cases:

  • Text generation
  • Language modeling
  • Natural language understanding tasks
  • Research and development in NLP

• Out-of-Scope Use Cases:

  • Real-time critical applications
  • High-stakes decision-making systems
  • Use in contexts where the model's output could be harmful or misleading

Factors

• Relevant Factors:

  • Model performance may vary across different languages and domains.
  • The model may generate biased or inappropriate content, especially in sensitive contexts.

• Evaluation Factors:

  • Performance on benchmark datasets
  • Human evaluation of generated text
  • Ethical considerations and potential biases

Limitations

• Known Limitations:
  • The model may generate biased or inappropriate content.
  • The model may not perform well on low-resource languages or specialized domains.
  • The model may require significant computational resources for inference.

Ethical Considerations

• Potential for Harm:

  • The model may generate harmful or biased content, especially in sensitive contexts.
  • The model should not be used in high-stakes decision-making systems.

• Mitigations:

  • Regularly evaluate the model for biases and ethical concerns.
  • Use the model in conjunction with human oversight.
  • Provide clear guidelines and warnings for users of the model.

How to Get Started with the Model

• Usage:

  from transformers import AutoTokenizer, AutoModelForCausalLM
  
  # Load the tokenizer and model
  tokenizer = AutoTokenizer.from_pretrained("bushai/sar-i-65b")
  model = AutoModelForCausalLM.from_pretrained("bushai/sar-i-65b")
  
  # Prepare the input text
  input_text = "Once upon a time"
  inputs = tokenizer(input_text, return_tensors="pt")
  
  # Generate text
  output = model.generate(**inputs, max_length=50)
  
  # Decode the output
  output_text = tokenizer.decode(output[0], skip_special_tokens=True)
  
  # Print the generated text
  print(output_text)```
  

• Dependencies:

  • transformers
  • torch