language:
- en
Prometh-MOEM-V.01 Model Card
Prometh-MOEM-V.01 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
To leverage Prometh-MOEM-V.01 in your projects, follow these steps:
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"])
### Merge configuration Details
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
positive_prompts: ["You excel in mathematical problem solving."]
Technical Specifications
Advanced Optimization
Quantization and Fine-Tuning: Prometh-MOEM-V.01 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-V.01 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.