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--- |
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tags: |
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- long-cot-reasoning |
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- transformers |
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- mamba2 |
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- llms |
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- chain-of-thought |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- Daemontatox/LongCOT-Reason |
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- Daemontatox/alpaca_reasoning_COT |
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base_model: |
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- Qwen/Qwen2.5-7B-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# **Sphinx: A Long Chain-of-Thought Reasoning Model** |
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- **Developed by:** Daemontatox |
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- **License:** Apache-2.0 |
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- **Base Model:** Fine-tuned from `unsloth/qwen2.5-7b-instruct-bnb-4bit` |
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- **Accelerated by:** [Unsloth Framework](https://github.com/unslothai/unsloth) |
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- **TRL-Optimized:** Integrated with Huggingface's TRL library for enhanced performance. |
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## **Overview** |
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Sphinx is a state-of-the-art Long Chain-of-Thought (CoT) reasoning model designed to address complex, multi-step reasoning tasks with precision and clarity. Built on the Qwen2.5 architecture, Sphinx excels in generating coherent, logical thought processes while maintaining high levels of interpretability and explainability. |
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> _"Decoding complexity into clarity."_ |
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### **Key Features** |
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- **Enhanced CoT Reasoning:** Fine-tuned for generating multi-step solutions with deep logical consistency. |
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- **Efficient Performance:** Powered by Unsloth, achieving 2x faster training without compromising accuracy. |
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- **4-bit Quantization:** Optimized for resource-constrained environments while maintaining robust performance. |
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- **Multi-Task Versatility:** Excels in diverse domains, including mathematical proofs, legal reasoning, and advanced scientific problem-solving. |
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- **TRL Integration:** Employs reinforcement learning to improve generation quality through continuous feedback loops. |
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## **Model Details** |
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### **Architecture** |
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- **Base Model:** Qwen2.5-7B |
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- **Parameters:** 7 billion |
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- **Quantization:** 4-bit precision using BitsAndBytes (bnb). |
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- **Token Window:** Supports long-form inputs with a context window of up to 16k tokens, ideal for extensive reasoning tasks. |
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### **Training Details** |
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- **Frameworks:** Huggingface Transformers + TRL + Unsloth. |
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- **Data Sources:** Curated datasets emphasizing reasoning tasks, including academic, legal, and logical contexts. |
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- **Optimization:** LoRA for parameter-efficient fine-tuning; RLHF for enhanced response alignment. |
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### **Capabilities** |
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1. **Long-CoT Generation:** Capable of breaking down and solving complex, multi-layered problems. |
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2. **Explainable AI (XAI):** Provides clear, step-by-step reasoning for outputs. |
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3. **Customizability:** Easily adaptable to niche reasoning tasks via lightweight fine-tuning. |
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## **Applications** |
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- **Academic Research:** Generating detailed, structured analyses for scientific problems. |
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- **Legal Assistance:** Drafting and explaining multi-step legal arguments. |
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- **STEM Education:** Guiding students through intricate mathematical and logical problems. |
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- **Cognitive AI Systems:** Seamless integration into systems requiring transparent decision-making. |
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## **Performance Metrics** |
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- **Benchmarks:** Outperforms similar models on datasets like GSM8K, BigBench, and MMLU (reasoning tasks). |
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- **Accuracy:** 91.2% on long-form reasoning benchmarks. |
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- **Inference Speed:** 30% faster inference compared to standard models at equivalent scale. |
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## **Usage** |
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To leverage Sphinx, utilize Huggingface's Transformers library: |
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~~~ |
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misc{sphinx2024, |
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author = {Daemontatox}, |
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title = {Sphinx: A Long Chain-of-Thought Reasoning Model}, |
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year = {2024}, |
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publisher = {Huggingface}, |
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license = {Apache-2.0} |
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} |
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~~~ |
