LeroyDyer

Language support includes English and Swahili.

Language: English. License: Apache 2.0.

Language: English. License: MIT.

Language: en License: openrail

This model is based on the transformers library and includes tags related to mergekit.

LeroyDyer/LCARSSTARFLEET-Q4KS-GGUF This model was converted to GGUF format from `LeroyDyer/LCARSSTARFLEET` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

Language: English. License: Apache 2.0.

This is a 7B parameter Mistral-based model designed to provide highly detailed, humanized responses with advanced reasoning capabilities. The model combines multiple specialized training approaches to create a versatile AI assistant capable of both technical tasks and natural conversation. - Context Length: 32k tokens (optimized for reliable performance at 4k chunks) - Multi-domain Expertise: Cross-trained on coding, medical, financial, and general problem-solving datasets - Humanized Responses: Trained on conversation patterns to provide more natural, empathetic interactions - Advanced Reasoning: Incorporates structured thinking patterns and step-by-step problem solving - Historical Knowledge: Specialized training on biblical texts, ancient documents, and archaeological materials The model employs a multi-stage training methodology: 1. Base Training: Foundation models merged for complementary capabilities 2. Specialized Training: Domain-specific datasets for expertise areas 3. Humanization Training: Conversation datasets to improve social interaction 4. Context Optimization: Training with varying context lengths to find optimal performance ranges Through extensive testing, we discovered that while the model supports 32k context, optimal performance occurs around 4k tokens. The model is designed to continue responses across multiple turns when needed, effectively managing longer conversations through segmentation. - LeroyDyer/Humanization001: Conversation patterns and social interaction training - LeroyDyer/QAOrganizedReasoningdataset001/002: Structured reasoning and problem-solving - Biblical and Ancient Texts: Complete biblical sources in multiple languages via SALT dataset - Archaeological Archives: Papers and translations from explorers and archaeologists The model supports agentic prompt patterns for complex problem-solving: The model includes experimental support for image-to-text conversion using Base64 encoding: The model includes comprehensive audio processing capabilities: - Temperature: 0.1-0.3 for analytical tasks, 0.7-0.9 for creative tasks - Max Tokens: 4096 for best performance (use "continue" for longer responses) - Context Window: Chunk inputs over 4k tokens across multiple interactions - Repetition Penalty: 1.1-1.15 to avoid repetitive responses 1. Structured Queries: Use clear, specific questions for best results 2. Context Management: Break long contexts into manageable chunks 3. Multi-turn Conversations: Utilize the model's ability to continue responses across turns 4. Expert Mode: Trigger specialized agents for domain-specific tasks 5. Reasoning Tasks: Use structured prompts with thinking tags for complex problems Through extensive testing, we discovered that models should ideally be trained with larger contexts, but practical performance often peaks at smaller token counts. The actual usable context length should be determined through empirical testing rather than theoretical maximums. Previous approaches using multiple model merging often led to corruption. The current approach focuses on one-to-one merging to ensure response quality and capability preservation. The model combines task-oriented efficiency with conversational naturalness, creating an AI that can both perform complex technical tasks and engage in meaningful dialogue. This dual capability makes it suitable for both professional and personal use cases. This model is part of the Spydaz Web AGI Project, a long-term initiative to build autonomous, multimodal, emotionally-aware AGI systems with fully internalized cognitive frameworks. The goal is to push boundaries in reasoning, decision-making, and intelligent tooling. Licensed under Apache 2.0. Created by Leroy Dyer as part of the SpydazWeb AI initiative. 📝 Citation bibtex @software{SpydazWebAIHumanAdvanced2024, author = {Leroy Dyer}, title = {SpydazWeb AI Human Advanced Model}, year = {2024}, url = {https://huggingface.co/LeroyDyer/SpydazWebAIHumanAdvanced}, note = {Multimodal AGI system with humanized interaction capabilities} } 📞 Support & Contact Documentation: Full technical specifications available Note: This model contains unfiltered historical and religious content from original sources. Implement appropriate safeguards for your application context. The model maintains academic integrity by presenting sources accurately while providing cultural and historical context.

Advanced student math model utilizing the transformers library with mergekit.

Creating Human Advance AI Success is a game of winners. The Human AI . (a lot of bad models to get to this one ! finally ) This model has been trained to respond in a more human manner as well as exhibit behaviours : it nows when to think and when not to think ! Some answers are direct and do not need the think and some are task based questions and need thinking ! So the model should not be stuck on a single response type ! SpydazWeb AI (7b Mistral) (Max Context 128k) This model has been trained to perform with contexts of 512k , although in training it has been trained mainly with the 2048 for general usage : A New genrea of AI ! This is Trained to give highly detailed humanized responses : Performs tasks well, a Very good model for multipupose use : the model has been trained to become more human in its reposes as well as role playing and story telling : This latest model has been trained on Conversations with a desire to respond with expressive emotive content , As well as discussions on various topics: It has also been focused on conversations by human interactions. hence there maybe NFSW contet in the model : This has no way inhibited its other tasks which were also aligned using the new intensive and Expressive prompt : AI aims to model human thought, a goal of cognitive science across fields like psychology and computer science. Thinking Rationally: AI also seeks to formalize “laws of thought” through logic, though human thinking is often inconsistent and uncertain. Acting Humanly: Turing's test evaluates AI by its ability to mimic human behavior convincingly, encompassing skills like reasoning and language. Acting Rationally: Russell and Norvig advocate for AI that acts rationally to achieve the best outcomes, integrating reasoning and adaptability to environments. Domains of Focus The model was trained with cross-domain expertise in: Our training approach encourages cognitive emulation, blending multiple reasoning modes into a single thought engine. We treat prompts not as mere inputs, but as process initiators that trigger multi-agent thinking and structured responses. Data Creation strategy is to combine the relevant datasets intot sinlge dataset and prompt setup ! A dataset can sway a model behaviour : the R1 Reasoning models can be a pain so we combine reasoning datasets with non reasoning datsets ... humanize the total datset before training th emodel on the new datset ! the tasks are generally Codeing and multistep reasoning tasks etc ! we have mixed rude and polite responses as weell as even some toxic responses and persona responses , ie based on a character or a expert perspective : the answer returned are TRUE ! these were often distilled from other models or datasets ! LONG PROMPT this prompt elicits the reasoning behaviour as well as aynalitical thining mechanizims GRAPHS ! graphs can be used also as prompts or within a prompt Giving examples of how tasks can be solved ! Agentic Prompt This prompt encourages themodel to generate expert teams to solve problems as well as setup virtual labs to safely simulate experiments : Examples of workflows that can be given for this prompt ! Competitive Code Review (Multi-Agent Adversarial) Intelligent Pattern: Agents compete to find the best solution. Reinforcement Learning for Customer Support (Adaptive Workflow) Intelligent Pattern: Agents learn from feedback to improve future runs. here we can convert images to text then use the text component in the query ! So we train on images converted to base64: then if a image is returned we can decode it from base64 base to a image : This methodology is painstaking : it requies mass images and conversions to text : But after training the task is embeded into the model : giving the model the possibility for such expansive querys as well as training the model on base64 information : Here we can even convert incoming dataset images to base64 on the fly Step 3: Extract Mel-Spectrogram from Image (Direct Pixel Manipulation) Step 7: Full Pipeline for Audio Processing with Customization This model is part of the Spydaz Web AGI Project, a long-term initiative to build autonomous, multimodal, emotionally-aware AGI systems with fully internalized cognitive frameworks. If your goal is to push boundaries in reasoning, decision-making, or intelligent tooling — this model is your launchpad.

- Developed by: LeroyDyer - License: apache-2.0 - Finetuned from model : LeroyDyer/SpydazWebONTOLOGYOFFICER This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Winners create more winners, while losers do the opposite. Success is a game of winners. SpydazWeb AI (7b Mistral) (Max Context 128k) This model has been trained to perform with contexts of 512k , although in training it has been trained mainly with the 2048 for general usage : A New genrea of AI ! This is Trained to give highly detailed humanized responses : Performs tasks well, a Very good model for multipupose use : the model has been trained to become more human in its reposes as well as role playing and story telling : This latest model has been trained on Conversations with a desire to respond with expressive emotive content , As well as discussions on various topics: It has also been focused on conversations by human interactions. hence there maybe NFSW contet in the model : This has no way inhibited its other tasks which were also aligned using the new intensive and Expressive prompt : AI aims to model human thought, a goal of cognitive science across fields like psychology and computer science. Thinking Rationally: AI also seeks to formalize “laws of thought” through logic, though human thinking is often inconsistent and uncertain. Acting Humanly: Turing's test evaluates AI by its ability to mimic human behavior convincingly, encompassing skills like reasoning and language. Acting Rationally: Russell and Norvig advocate for AI that acts rationally to achieve the best outcomes, integrating reasoning and adaptability to environments. Domains of Focus The model was trained with cross-domain expertise in: Our training approach encourages cognitive emulation, blending multiple reasoning modes into a single thought engine. We treat prompts not as mere inputs, but as process initiators that trigger multi-agent thinking and structured responses. here we can convert images to text then use the text component in the query ! So we train on images converted to base64: then if a image is returned we can decode it from base64 base to a image : This methodology is painstaking : it requies mass images and conversions to text : But after training the task is embeded into the model : giving the model the possibility for such expansive querys as well as training the model on base64 information : Here we can even convert incoming dataset images to base64 on the fly Step 3: Extract Mel-Spectrogram from Image (Direct Pixel Manipulation) Step 7: Full Pipeline for Audio Processing with Customization This model is part of the Spydaz Web AGI Project, a long-term initiative to build autonomous, multimodal, emotionally-aware AGI systems with fully internalized cognitive frameworks. If your goal is to push boundaries in reasoning, decision-making, or intelligent tooling — this model is your launchpad.

- Developed by: LeroyDyer - License: apache-2.0 - Finetuned from model : LeroyDyer/SpydazWebLCARSArtificialHumanR1002-Multi-lingual-Thinking This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.