--- language: en license: mit tags: - chain-of-thought - structured-response - causal-lm - text-generation datasets: - diverse pipeline_tag: text-generation model_name: state-0 library_name: transformers metrics: - accuracy - character inference: true --- # State-0: A chain-of-thoughts-based 8B alternative to GPT-o1 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/124hfluZIrtVeZ-gWJEz6C_6nhfFpUBhY?usp=sharing) [![Read Release Note](https://img.shields.io/badge/Read-Release%20Note-brightgreen)](https://exthalpy.com/2024/09/18/introducing-state-0-exthalpys-advanced-chain-of-thought-ai-model-on-hugging-face/) ## Model Card - **Model Name**: State-0 - **Version**: 1.0 - **Author**: Udit Akhouri - **Hugging Face Model Page**: [Exthalpy/state-0](https://huggingface.co/Exthalpy/state-0/) - **Architecture**: 8b core parameters with an additional 40 million parameters - **Training Data**: Diverse datasets across various domains - **Capabilities**: Chain-of-thought reasoning, Socratic instincts, in-depth and structured responses - **Competitive Benchmark**: Capable of matching and surpassing the reasoning ability of GPT-4o1 - **Applications**: Educational tools, research, analytical problem-solving, and more - **License**: MIT License ## Abstract State-0 is a novel chain-of-thought language model, designed to emulate structured human-like reasoning in its responses. Inspired from the robust architecture of Llama 3.1 8b and enhanced with over 40 million additional parameters, State-0 achieves a significant leap in cognitive capabilities. It incorporates "Socratic instincts" to dissect complex queries methodically and arrive at well-rounded conclusions. Competing with the reasoning prowess of GPT-4o1, State-0 not only provides accurate answers but also elucidates the logical pathways leading to those answers, making it a powerful tool for applications requiring in-depth analysis and clarity. ## 1. Introduction The field of natural language processing (NLP) has been significantly advanced by large language models (LLMs) capable of generating human-like text. However, most LLMs still lack the ability to break down complex queries into multiple facets, analyze them, and synthesize a comprehensive answer. State-0 addresses this limitation by employing a chain-of-thought reasoning mechanism combined with Socratic instincts. This paper introduces the architecture, training, and capabilities of State-0, demonstrating its competitive edge against models like GPT-4o1 in structured thinking and problem-solving. ## 2. Model Architecture State-0, fundamentally inspired by Llama 3.1 8b, is augmented with over 40 million chain-of-thoughts and socratic instinct parameters. These parameters are meticulously trained to enhance the model's ability to reason, question, and deduce, drawing from vast datasets encompassing diverse fields of knowledge. ### 2.1 Enhancements Over Llama 3.1 8b - **Additional Parameters**: State-0 incorporates 40 million additional parameters specifically fine-tuned to improve logical reasoning and analytical depth. - **Chain-of-Thought Mechanism**: The model leverages a multi-step process for breaking down queries into manageable components, similar to human analytical thinking. - **Socratic Instincts**: Inspired by the Socratic method, State-0 probes different aspects of a question, leading to a comprehensive and reasoned conclusion. ## 3. Model Capabilities ### 3.1 Chain-of-Thought Reasoning State-0 excels at decomposing complex questions into simpler elements. It addresses these components individually and synthesizes the answers into a coherent response. **Example**: **Prompt**: Is milk a good source of protein? **Response**: 1. **Definition**: Milk is a dairy product produced by mammals, including cows, goats, and sheep. 2. **Protein Content**: Milk is a rich source of protein, providing essential amino acids. 3. **Importance**: Protein is necessary for muscle growth, tissue repair, and immune function. **Conclusion**: Milk is a valuable source of protein and contributes to overall health. ### 3.2 Competing with GPT-4o1 State-0 demonstrates competitive performance in terms of analytical depth and reasoning, often surpassing models like GPT-4o1 in its ability to provide contextually relevant and logically sound answers. ## 4. Getting Started State-0 is available for use via the Hugging Face `transformers` library. This section outlines the installation and usage process for integrating State-0 into your projects. ### 4.1 Installation Ensure you have the `transformers` library installed: ```bash pip install transformers ``` ### 4.2 Usage #### High-Level Pipeline State-0 can be easily used with the high-level pipeline API for text generation: ```python from transformers import pipeline pipe = pipeline("text-generation", model="uditakhouri/state-0") response = pipe("Is milk a good source of protein?") print(response) ``` #### Direct Model Loading For more control, State-0 can be loaded directly using the following code: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("uditakhouri/state-0") model = AutoModelForCausalLM.from_pretrained("uditakhouri/state-0") input_text = "Is milk a good source of protein?" input_ids = tokenizer.encode(input_text, return_tensors="pt") output = model.generate(input_ids, max_length=100) response = tokenizer.decode(output[0], skip_special_tokens=True) print(response) ``` ## 5. Training Details State-0 was trained using a diverse set of datasets, fine-tuned to enhance its reasoning and conversational abilities. The training process focused on: - Reinforcement Learning from Human Feedback (RLHF) for nuanced responses. - Incorporating various fields of knowledge, from basic concepts to complex theories, to create a versatile reasoning engine. ## 6. Socratic Instincts Inspired by the Socratic method, State-0 is designed to think through different scenarios and perspectives before arriving at an answer. This is achieved through: - **Multi-Step Processing**: Breaking down a question into smaller parts, analyzing each component, and then synthesizing an answer. - **Self-Interrogation**: The model internally queries different aspects of a topic, ensuring a balanced and well-thought-out response. ## 7. Evaluation and Results State-0 has been rigorously tested against existing models like GPT-4o1, showing a high level of competence in chain-of-thought reasoning. It provides not only accurate answers but also the logical pathway leading to those answers, setting a new benchmark in LLM reasoning. ## 8. Conclusion State-0 represents a significant advancement in the field of NLP by integrating chain-of-thought reasoning and Socratic instincts into its framework. With its enhanced parameters and structured analytical capabilities, State-0 is a formidable model for applications that demand a deep and reasoned understanding of complex queries. ## 9. Future Work Future versions of State-0 aim to further enhance its reasoning capabilities by incorporating more advanced cognitive models and expanding its knowledge base. ## 10. License State-0 is released under the MIT License. ## 11. References For a complete list of references and further reading, please visit the model's page on [Hugging Face](https://huggingface.co/uditakhouri/state-0). ## 12. Contact For inquiries, collaborations, or further information, please contact Udit Akhouri.