slices:
  - sources:
      - model: Or4cl3-1/Cognitive-Agent-Gemma_7b
        layer_range: [0, 62]
      - model: Or4cl3-1/agent_gemma_7b
        layer_range: [0, 62]
merge_method: slerp
base_model: Or4cl3-1/Cognitive-Agent-Gemma_7b
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Or4cl3-1/cognitiv-agent_1"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

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"])

Model Card

Model Name: cognitiv-agent_1 Model Version: 1.0 Model Type: Text Generation Model Architecture: Hybrid Learning Engine, Multimodal Communication Interface

Overview

The cognitiv-agent_1 model is a merge of two underlying models, Or4cl3-1/Cognitive-Agent-Gemma_7b and Or4cl3-1/agent_gemma_7b, utilizing the LazyMergekit technique. It is designed for text generation tasks and is capable of producing coherent and contextually relevant responses to user prompts.

Model Composition

Or4cl3-1/Cognitive-Agent-Gemma_7b
Or4cl3-1/agent_gemma_7b

Configuration

The model is configured using the following parameters:

Merge Method: slerp (spherical linear interpolation)
Layer Range: [0, 62] for both models
Parameters:
- t:
  - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1]
  - filter: mlp value: [1, 0.5, 0.7, 0.3, 0]
  - value: 0.5
Data Type: bfloat16

License

This model is released under the Apache License, Version 2.0.

Usage

The model can be used for text generation tasks using the provided Python code snippet. It requires the transformers and accelerate libraries. Users can input prompts and receive generated text responses.

Ethical Considerations

As with any AI model, there are ethical considerations to take into account when using the cognitiv-agent_1 model. These include:

Bias Mitigation: Ensure the model is trained on diverse and representative data to mitigate bias in generated outputs.
Privacy: Respect user privacy and confidentiality when processing user-generated prompts.
Fair Use: Use the model responsibly and avoid generating harmful or inappropriate content.

Limitations

Performance: The model's performance may vary depending on the complexity and specificity of the input prompts.
Understanding: While the model can generate contextually relevant responses, it may not fully understand the nuances or underlying meaning of the input prompts.

Contact Information

For inquiries or support regarding the cognitiv-agent_1 model, please contact Or4cl3 AI Solutions at contact@or4cl3.com.

Or4cl3-1/cognitive-agent_1

cognitiv-agent_1

🧩 Configuration