Prometh-MOEM-24B Model Card

Prometh-MOEM-24B is a Mixture of Experts (MoE) model that integrates multiple foundational models to deliver enhanced performance across a spectrum of tasks. It harnesses the combined strengths of its constituent models, optimizing for accuracy, speed, and versatility.

Model Sources and Components

This MoE model incorporates the following specialized models:

• Language translation
• Question answering

💻Usage Instructions

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("AIFS/Prometh-MOEM-24B")
model = AutoModelForCausalLM.from_pretrained("AIFS/Prometh-MOEM-24B")

# Set up the pipeline
text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Generate text
prompt = "The future of AI in healthcare is"
generated_texts = text_generator(prompt, max_length=50, num_return_sequences=3)

for generated_text in generated_texts:
    print(generated_text["generated_text"])

Technical Specifications

Advanced Optimization

Quantization and Fine-Tuning: Prometh-MOEM-24B can be fine tuned, offering pathways for both quantization and fine-tuning. These processes refine the model's performance and efficiency, catering to the nuanced demands of deployment environments.

Quantization

Quantization is a technique aimed at reducing the computational and memory burdens of model inference. It achieves this feat by transitioning from high-precision data types, like 32-bit floating point (float32), to more compact and efficient formats, such as 8-bit integers (int8). This transition not only shrinks the model's memory footprint but also accelerates its operational pace, making it more viable for embedded systems or devices with limited computational resources.

• Benefits:
• Application:
  • Prometh-MOEM-24B can be quantized post-training, adjusting to int8 without retraining from scratch. This method preserves the essence of its intelligence while adapting to the practical constraints of deployment environments.

Fine-Tuning

Beyond quantization, the model is primed for fine-tuning, allowing it to adapt to specific tasks or datasets with increased precision. This process involves additional training cycles on new data, thereby enhancing its acumen for particular applications.

• Customization: Tailors the model to specialized needs, optimizing its performance on tasks it was not originally designed for.
• Versatility: Ensures the model remains relevant and effective across a diverse array of use cases.

Model Details and Attribution

• Developed by: [Iago Gaspar]
• Shared by: [AI Flow Solutions]
• Model type: Mixture of Experts Model
• Language(s) (NLP): en-en
• License: Apache-2.0

Environmental Impact

Out-of-Scope Use

The model is not intended for generating harmful or biased content.

Bias, Risks, and Limitations

Recommendations

Users should evaluate the model for biases and other ethical considerations before deploying it for real-world applications.