metadata

license: apache-2.0
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:

Key Features

Enhanced Performance: Specifically optimized for superior accuracy and efficiency in processing.
Versatility: Exhibits outstanding versatility across a broad array of NLP tasks.
State-of-the-Art Integration: Employs the latest in AI research to integrate multiple models effectively.

Application Areas

Prometh-MOEM-V.01 excels in various applications, including:

Text generation
Sentiment analysis
Language translation
Question answering

💻Usage Instructions

To leverage Prometh-MOEM-V.01 in your projects, follow these steps:

pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer, pipeline
import torch

model = "Wtzwho/Prometh-MOEM-V.01"
tokenizer = AutoTokenizer.from_pretrained(model)

# Setting up the pipeline
pipeline = pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

# Example query
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

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

base_model: mistralai/Mistral-7B-Instruct-v0.2
gate_mode: hidden
dtype: bfloat16
experts_per_token: 2
experts:
  - source_model: Wtzwho/Prometh-merge-test2
    positive_prompts: ["You are a helpful general-purpose assistant."]
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts: ["You provide instruction-based assistance."]
  - source_model: Wtzwho/Prometh-merge-test3
    positive_prompts: ["You are helpful for coding-related queries."]
  - source_model: meta-math/MetaMath-Mistral-7B
    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:
- Reduced Memory Footprint: Occupies less storage, making the model more deployable on resource-constrained platforms.
- Enhanced Performance: Boosts inference speed due to the efficiency of integer arithmetic operations.
- Energy Efficiency: Consumes less power, a critical factor for mobile and embedded applications.
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.