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