Uploaded model - AlphaAI-Reason-V0

Model Overview

"AlphaAI-Reason-V0" is a language model fine-tuned over LLaMA 3-3B-Instruct, designed to handle complex reasoning tasks such as logical problem-solving, mathematical analysis, and structured explanations. The model has been trained on a diverse dataset of multi-turn conversations focused on in-depth reasoning, making it highly effective for tasks requiring step-by-step thought processes.

Key Features

• Advanced Reasoning: Trained to provide structured and coherent responses, breaking down complex problems into logical steps.
• Chain-of-Thought Processing: The model may generate tokens to illustrate its intermediate reasoning, making its thought process transparent.
• Mathematical and Logical Proficiency: Capable of handling problems in formal logic, mathematics, and structured argumentation.
• Context-Aware Problem Solving: Processes multi-turn interactions to build upon previous exchanges and provide well-informed answers.
• Versatile Applications: Suitable for domains requiring deep analytical capabilities, including research, academic support, and decision-making workflows.

Usage Considerations

When deploying "AlphaAI-Reason-V0" note that responses may contain tokens, which serve as markers for the model's internal reasoning steps. If using the model in custom applications, these tokens may need to be preprocessed or filtered depending on your use case. Additionally, since the model is optimized for reasoning, responses may be more detailed compared to general-purpose language models.

Limitations

• Not a Large Model – Being a small 3B-parameter model, it may struggle with highly complex, multi-layered reasoning tasks that require deeper contextual awareness.
• Performance Variability – While optimized, some edge cases requiring extensive memory, nuanced abstraction, or long-term dependencies may result in inconsistent outputs.
• Processing Speed vs. Accuracy – Running the model on low-end devices may trade-off between response time and accuracy, depending on the quantization used.
• Requires Post-Processing for Tokens – The model explicitly demonstrates its reasoning, which may require custom handling when integrating into consumer-facing applications.

Ethical Considerations

While "AlphaAI-Reason-V0" is designed to provide accurate and well-structured reasoning, users should verify its outputs before relying on them for critical decisions. The model generates responses based on learned patterns and may sometimes exhibit biases or inaccuracies. Users should critically evaluate its outputs and apply domain-specific validation.

Model Availability

"AlphaAI-Reason-V0" is available in the following quantized formats:

• q4_k_m
• q5_k_m
• 16 bit (This)

Quantized version - https://huggingface.co/alphaaico/AlphaAI-Reason-V0-GGUF

These quantized versions provide flexibility in deployment, balancing efficiency and accuracy based on hardware constraints and performance needs.

"AlphaAI-Reason-V0" is designed to enhance applications requiring structured reasoning and logical processing. Explore its capabilities to integrate advanced AI-driven solutions into your workflow.

Sample Prompts to experience the "Thinking" capability

• The weight of a packaged cereal box follows a normal distribution with a mean of 500 grams and a standard deviation of 15 grams. What is the probability that a randomly selected box weighs more than 520 grams?
• A customer service call center records the duration of calls, which follows a normal distribution with an average length of 12 minutes and a standard deviation of 2.5 minutes. What is the probability that a randomly chosen call lasts longer than 15 minutes?
• The duration of a process used to manufacture components is known to be normally distributed with a mean of 30 minutes and a standard deviation of 4 minutes. What is the probability of a time greater than 33 minutes being recorded?

Ideal System Prompt

You are an expert in logical and mathematical problem-solving. For every query you receive, break down the problem into smaller parts, analyze each component carefully, and include your full reasoning within and tokens. Always ensure that your step-by-step analysis enclosed in and tokens is part of your final response. Once the reasoning is done, then you generate the final response.

Follow this structured approach:

1. Deconstruct the Problem:

• Identify key elements, such as variables, constraints, and objectives.
• Consider the relationships between these elements and any implicit or explicit rules.

2. Analyze Information Dynamics:

• Determine what information is available and to whom.
• Model how this information can be used to make deductions or draw conclusions.

3. Test Hypothetical Scenarios:

• Explore different possibilities using a process of elimination or other systematic methods.
• Evaluate the implications of each scenario on the problem's solution.

4. Identify Critical Actions or Insights:

• Determine which steps or realizations are crucial to solving the problem.
• Assess how these actions or insights contribute to finding a solution.

5. Guarantee a Solution (if applicable):

• Verify that the proposed solution strategy is valid and applicable to the problem.
• Ensure that the solution is robust and can be generalized if necessary.

Response Format:

• Use <think>...</think> for step-by-step analysis.
• Conclude with Final Answer: outside the think tags, providing a clear and concise solution or conclusion based on the analysis.

Example Use Case: If a user submits a query like "What is the optimal strategy for a given game or puzzle?", the response would follow the above structure, deconstructing the problem, analyzing information dynamics, testing scenarios, identifying critical actions, and guaranteeing a solution if applicable.

<think> To demonstrate this generalized approach, let's consider a generic problem-solving scenario:

1. Deconstruct the Problem:

• Identify the key elements, such as the problem's objectives, constraints, and variables.
• Analyze the relationships between these elements and the rules governing the problem.

2. Analyze Information Dynamics:

• Assess what information is available and how it can be utilized to make deductions.
• Model the flow of information and its implications for solving the problem.

3. Test Hypothetical Scenarios:

• Explore different hypothetical situations to understand their impact on the problem's solution.
• Evaluate the outcomes of each scenario to identify patterns or critical insights.

4. Identify Critical Actions or Insights:

• Determine the essential steps or realizations necessary for solving the problem.
• Evaluate how these actions or insights contribute to finding a solution.

5. Guarantee a Solution (if applicable):

• Verify that the proposed solution strategy is valid and applicable to the problem.
• Ensure the solution is robust and can be generalized if necessary. </think>

Final Answer: The final answer will depend on the specific problem being solved, following the step-by-step analysis and structured approach outlined above.

Note: The UI used is LMStudio (https://lmstudio.ai/)