78 lines
2.7 KiB
Markdown
78 lines
2.7 KiB
Markdown
---
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library_name: transformers
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tags:
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- sft
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- unsloth
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- science
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- reasoning
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license: apache-2.0
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datasets:
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- mattwesney/CoT_Reasoning_Scientific_Discovery_and_Research
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language:
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- en
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base_model:
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- khazarai/Scie-R1
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pipeline_tag: text-generation
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---
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# Model Card for Qwen3-CoT-Scientific-Research
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## Model Description
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GGUF version of https://huggingface.co/khazarai/Scie-R1
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- **Base Model:** Qwen3-1.7B
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- **Task:** Scientific Reasoning with Chain-of-Thought (CoT)
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- **Dataset:** [moremilk/CoT_Reasoning_Scientific_Discovery_and_Research](https://huggingface.co/datasets/moremilk/CoT_Reasoning_Scientific_Discovery_and_Research)
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- **Training Objective:** Encourage step-by-step logical deductions for scientific reasoning problems
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## Uses
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### Direct Use
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This fine-tuned model is designed for:
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- Assisting in teaching and learning scientific reasoning
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- Supporting educational AI assistants in science classrooms
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- Demonstrating step-by-step scientific reasoning in research training contexts
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- Serving as a resource for automated reasoning systems to better emulate structured scientific logic
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It is not intended to replace human researchers, perform advanced analytics, or generate novel scientific discoveries.
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## Bias, Risks, and Limitations
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- May oversimplify complex or interdisciplinary problems
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- Performance limited by the scope of training data (primarily introductory-level scientific reasoning tasks)
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- Does not handle real-world experimentation or advanced statistical modeling
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- May produce incorrect reasoning if the prompt is highly ambiguous
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## Training Data
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**Scope**
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This model was fine-tuned on tasks that involve core scientific reasoning:
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- Formulating testable hypotheses
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- Identifying independent and dependent variables
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- Designing simple controlled experiments
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- Interpreting graphs, tables, and basic data representations
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- Understanding relationships between evidence and conclusions
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- Recognizing simple logical fallacies in scientific arguments
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**Illustrative Examples**
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- Drawing conclusions from experimental results
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- Evaluating alternative explanations for observed data
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- Explaining step-by-step reasoning behind scientific conclusions
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**Emphasis on Chain-of-Thought (CoT)**
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- The dataset highlights explicit reasoning steps, making the model better at producing step-by-step explanations when solving scientific reasoning tasks.
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- Focus on Foundational Knowledge
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- The dataset aims to strengthen models in foundational scientific reasoning skills rather than covering all domains of scientific knowledge.
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**Focus on Foundational Knowledge**
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The dataset aims to strengthen models in foundational scientific reasoning skills rather than covering all domains of scientific knowledge. |