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Model: Rustamshry/Scie-R1-GGUF
Source: Original Platform

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README.md

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+---
+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](https://huggingface.co/datasets/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.

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