Update README.md

README.md

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 This model was converted to GGUF format from [`Spestly/Atlas-Flash-7B-Preview`](https://huggingface.co/Spestly/Atlas-Flash-7B-Preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/Spestly/Atlas-Flash-7B-Preview) for more details on the model.
 
+Atlas-Flash is the first model in the Atlas family, a new generation of AI systems designed to excel in tasks requiring advanced reasoning, contextual understanding, and domain-specific expertise. Built on Deepseek's R1 distilled Qwen models, Atlas-Flash integrates state-of-the-art methodologies to deliver significant improvements in coding, conversational AI, and STEM problem-solving.
+
+With a focus on versatility and robustness, Atlas-Flash adheres to the core principles established in the Athena project, emphasizing transparency, fairness, and responsible AI development.
+Model Details
+
+    Base Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+    Parameters: 7 Billion
+    License: MIT
+
+Key Features
+
+    Improved Coding Capabilities
+        Supports accurate and efficient code generation, debugging, code explanation, and documentation writing.
+        Handles multiple programming languages and frameworks with strong contextual understanding.
+        Excels at solving algorithmic problems and generating optimized solutions for software development tasks.
+
+    Advanced Conversational Skills
+        Provides natural, context-aware, and coherent multi-turn dialogue.
+        Handles both informal chat and task-specific queries with adaptability.
+        Can summarize, clarify, and infer meaning from conversational input, enabling dynamic interaction.
+
+    Proficiency in STEM Domains
+        Excels in solving complex problems in mathematics, physics, and engineering.
+        Capable of explaining intricate concepts with clarity, making it a useful tool for education and technical research.
+        Demonstrates strong reasoning skills in tasks requiring logic, pattern recognition, and domain-specific expertise.
+
+Training Details
+
+Atlas-Flash underwent extensive training on a diverse set of high-quality datasets to ensure broad domain coverage and exceptional performance. The training process prioritized both generalization and specialization, leveraging curated data for coding, conversational AI, and STEM-specific tasks.
+Datasets Used:
+
+    BAAI/TACO
+        A robust natural language dataset designed for language understanding and contextual reasoning.
+        Enables the model to excel in tasks requiring deep comprehension and nuanced responses.
+
+    rubenroy/GammaCorpus-v1-70k-UNFILTERED
+        A large-scale, unfiltered corpus that provides a diverse range of real-world language examples.
+        Ensures the model can handle informal, technical, and domain-specific language effectively.
+
+    codeparrot/apps
+        A dataset built for programming tasks, covering a wide range of coding challenges, applications, and practical use cases.
+        Ensures high performance in software development tasks, including debugging, optimization, and code explanation.
+
+    Hand-Collected Synthetic Data
+        Curated datasets tailored to specific tasks for fine-tuning and specialization.
+        Includes challenging edge cases and rare scenarios to improve model adaptability and resilience.
+
+Training Methodology
+
+    Distillation from Qwen Models: Atlas-Flash builds on Deepseek's distilled Qwen models, inheriting their strengths in language understanding and multi-domain reasoning.
+    Multi-Stage Training: The training process included multiple stages of fine-tuning, focusing separately on coding, general language tasks, and STEM domains.
+    Synthetic Data Augmentation: Hand-collected synthetic datasets were used to supplement real-world data, ensuring the model is capable of handling corner cases and rare scenarios.
+    Iterative Feedback Loop: Performance was iteratively refined through evaluation and feedback, ensuring robust and accurate outputs across tasks.
+
+Applications
+
+Atlas-Flash is designed for a wide range of use cases:
+1. Software Development
+
+    Code generation, optimization, and debugging.
+    Explaining code logic and writing documentation.
+    Automating repetitive tasks in software engineering workflows.
+
+2. Conversational AI
+
+    Building intelligent chatbots and virtual assistants.
+    Providing context-aware, coherent, and natural multi-turn dialogue.
+    Summarizing conversations and supporting decision-making in interactive systems.
+
+3. STEM Problem-Solving
+
+    Solving mathematical problems with step-by-step explanations.
+    Assisting with physics, engineering, and data analysis tasks.
+    Supporting scientific research through technical insights and reasoning.
+
+4. Education and Knowledge Assistance
+
+    Simplifying and explaining complex concepts for learners.
+    Acting as a virtual tutor for coding and STEM disciplines.
+    Providing accurate answers to general knowledge and domain-specific queries.
+
+Strengths
+
+    Versatility: Performs exceptionally well across multiple domains, including coding, conversational AI, and STEM tasks.
+    Contextual Understanding: Handles nuanced and multi-turn interactions with strong comprehension.
+    High Accuracy: Delivers precise results for complex coding and STEM challenges.
+    Adaptability: Capable of generating creative and optimized solutions for diverse use cases.
+
+Limitations
+
+While Atlas-Flash demonstrates significant advancements, it has the following limitations:
+
+    Bias in Training Data: Despite efforts to curate high-quality datasets, biases in the training data may occasionally influence outputs.
+    Context Length Constraints: The model may struggle with extremely long documents or conversations that exceed its maximum context window.
+    Domain-Specific Knowledge Gaps: While Atlas-Flash is versatile, it may underperform in highly niche or specialized domains that were not sufficiently represented in the training data.
+    Dependence on Input Quality: The model's performance depends on the clarity and coherence of the input provided by the user.
+
+Ethical Considerations
+
+    Misuse Prevention: Users are expected to employ Atlas-Flash responsibly and avoid applications that could cause harm or violate ethical guidelines.
+    Transparency and Explainability: Efforts have been made to ensure the model provides clear and explainable outputs, particularly for STEM and coding tasks.
+    Bias Mitigation: While biases have been minimized during training, users should remain cautious and critically evaluate outputs for fairness and inclusivity.
+
+Future Directions
+
+As the first model in the Atlas family, Atlas-Flash establishes a strong foundation for future iterations. Planned improvements include:
+
+    Expanded Training Data: Integration of more diverse and niche datasets to address knowledge gaps.
+    Improved Context Management: Enhancements in handling long-context tasks and multi-turn conversations.
+    Domain-Specific Fine-Tuning: Specialization in areas such as healthcare, legal, and advanced scientific research.
+    Atlas-Pro: Atlas-Pro is meant to be built on Atlas-Flash to provide excellent reasoning when answering questions
+
+Conclusion
+
+Atlas-Flash is a versatile and robust model that sets new benchmarks in coding, conversational AI, and STEM problem-solving. By leveraging Deepseek's R1 distilled Qwen models and high-quality datasets, it offers exceptional performance across a wide range of tasks. As the first model in the Atlas family, it represents a significant step forward, laying the groundwork for future innovations in AI development.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)