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README.md

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+
+---
+
+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
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/state-0-GGUF
+This is quantized version of [Exthalpy/state-0](https://huggingface.co/Exthalpy/state-0) created using llama.cpp
+
+# Original Model Card
+
+
+
+
+
+# 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.