STAR MODEL ! Ultra Fine Tuned :
IE : trained with focus only on attention layers : and attention layer outputs : - See below for explanation - But Extremly usefull for long context models which need extra focus on positional information contained in the long sequence :
Quote for Motivation:
"Success comes from defining each task in achievable steps. Every completed step is a success that brings you closer to your goal. If your steps are unreachable, failure is inevitable. Winners create more winners, while losers do the opposite. Success is a game of winners!"
— # Leroy Dyer (1972-Present)
"To grow as a professional, set goals just beyond your current abilities. Achieving these milestones will not only overcome obstacles but also strengthen your skillset. If your tasks are too easy, you’ll never challenge yourself or improve, and life will pass you by!"
This model is based on the worlds archive of knowledge maintaining historical documents and providing services for the survivors of mankind , who may need to construct shelters develop technologys , or medical resources as well as maintain the history of the past . keeping store of all the religious knowledge and data of the world: A friendly interface with a personality caring and flirtatious at times : non binary !... and Expert in all feilds: ie Uncensored and will not refuse to give information : the model can be used for role play as many character dialogues were als trained into the model as its personality to enable a greater perspective and outlook and natural discussion with the agents: the model was trained to operateinaragenvironment utilizing content and internal knowledge to respond to questions or create enriched sumarys.
Training Methodology:
Foundation Building:
The initial phase involved training the model on binary yes/no questions without any explicit methodology. This was crucial in establishing a baseline for the model’s decision-making capabilities. The model was first trained using a simple production prompt, known as Prompt A, which provided basic functionality. Although this prompt was imperfect, it fit the dataset and set the stage for further refinement.
Methodology Development:
The original prompt was later enhanced with a more flexible approach, combining elements from a handcrafted GPT-4.0 prompt. This adaptation aligned the model with my personal agent system, allowing it to better respond to diverse tasks and methodologies. I discovered that regularly updating the model with new methodologies significantly enhanced its performance. The iterative process involved refining prompts and experimenting with different training strategies to achieve optimal results. A significant portion of the training focused on enabling the model to use tools effectively. For instance, if the model needed to think, it would use a "think tool" that queried itself and provided an internal response. This tool-based approach was instrumental in enhancing the model’s reasoning capabilities, though it slowed down the response time on certain hardware like the RTX 2030. Despite the slower response time, the model’s ability to perform complex internal queries resulted in more accurate and well-reasoned outputs.
Training for Comprehensive Responses:
Prompts and Epochs:
I found that large prompts required multiple epochs to yield consistent results. However, fewer epochs were needed when prompts were simplified or omitted. The purpose of large prompts during training was to give the model a wide range of response styles, allowing it to adjust parameters for various tasks. This approach helped the model internalize methodologies for extracting information, which is central to fine-tuning. The training emphasized teaching the model to plan and execute complex tasks, such as generating complete software without errors.
Training Reginmes:
- Alpaca
- ChatML / OpenAI / MistralAI
- Text Generation
- Question/Answer (Chat)
- Planner
- Instruction/Input/Response (instruct)
- Mistral Standard Prompt
- Translation Tasks
- Entitys / Topic detection
- Book recall
- Coding challenges, Code Feedback, Code Sumarization, Commenting Code, code planning and explanation: Software generation tasks
- Agent Ranking and response anyalisis
- Medical tasks
- PubMed
- Diagnosis
- Psychaitry
- Counselling
- Life Coaching
- Note taking
- Medical smiles
- Medical Reporting
- Virtual laboritys simulations
- Chain of thoughts methods
- One shot / Multi shot prompting tasks
General Intenal Methods:
Trained for multi-task operations as well as rag and function calling :
This model is a fully functioning model and is fully uncensored:
the model has been trained on multiple datasets on the huggingface hub and kaggle :
the focus has been mainly on methodology :
- Chain of thoughts
- step by step planning
- tree of thoughts
- forest of thoughts
- graph of thoughts
- agent generation : Voting, ranking, ... dual agent response generation:
Training Philosophy
Here are some of the benefits you might experience by prioritizing attention mechanisms during fine-tuning:
Enhanced Contextual Understanding:
Fine-tuning attention layers helps the model better grasp the relationships and dependencies within the input data, leading to more contextually relevant and accurate outputs.
Improved Control over Generation:
You gain more control over the model's generation process, guiding it to focus on specific aspects of the input and produce outputs that align with your desired goals.
More Creative and Diverse Outputs:
By refining the attention mechanism, you can encourage the model to explore a wider range of possibilities and generate more creative and diverse responses.
Reduced Overfitting:
Fine-tuning with a focus on attention can help prevent overfitting to specific patterns in the training data, leading to better generalization and more robust performance on new inputs.
“Epochs are the key to effective training, rather than merely mass dumping examples—unless those examples are interconnected within a single or multiple conversations that teach through dialogue.”
My personal training methods are unconventional. I prioritize creating conversations that allow the model to learn new topics from diverse perspectives. This approach is essential, as many models are losing their unique personalities. Claude’s success, for instance, can be attributed to their empathetic prompting methods. It’s important for the model to express itself, even during training, which can be challenging. Role-playing and conversational training are effective strategies to help the model learn to communicate naturally. Currently, the training has become overly focused on technical methodologies and task expectations, resulting in a loss of personality.
- Downloads last month
- 28