Duplicate from Supply-Chain-AI-Research/GSCF_finetune

Co-authored-by: Vince Castillo <ProfessorCastillo@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+---
+
+# GSCF Q&A Dataset for Fine-Tuning
+
+## Dataset Summary
+
+This dataset contains a collection of question-and-answer pairs specifically designed for fine-tuning Large Language Models (LLMs) on the **Global Supply Chain Forum (GSCF) framework**, a leading process model developed at The Ohio State University.
+
+The data is structured to train a model to act as an expert supply chain consultant. The content covers two main types of interactions:
+
+1.  **Definitional Knowledge:** Questions that define and explain the core concepts, goals, and activities of the eight GSCF processes.
+2.  **Scenario-Based Application:** Realistic business problems that require the model to identify the relevant GSCF processes and formulate a structured, actionable solution based on the framework's principles.
+
+The goal of this dataset is to create a specialized AI assistant capable of performing nuanced analysis and providing expert-level advice within the GSCF methodology.
+
+## Dataset Structure
+
+The dataset is provided in the `.jsonl` format, where each line is a distinct JSON object. Each object contains two keys:
+
+-   `"question"`: A string representing the user's query or problem statement.
+-   `"answer"`: A string containing the expert, GSCF-aligned response.
+
+### Example Data Point
+
+```json
+{
+  "question": "Our retail customers complain about inconsistent product availability across stores. How can we address this?",
+  "answer": "The GSCF (Global Supply Chain Forum) framework identifies this challenge as falling under **Demand Management** and **Order Fulfillment** processes. Demand Management focuses on accurate forecasting and inventory balancing, while Order Fulfillment ensures efficient distribution. You're likely experiencing forecast inaccuracies and poor allocation logic. Implement point-of-sale data analytics to capture real-time demand signals and adjust inventory dynamically. For a fashion retailer, this might mean redistributing seasonal items between urban and suburban stores based on weekly sales velocity. Establish safety stock policies tailored to each store's demand variability and lead times. Cross-functional collaboration between merchandising and logistics teams is crucial—use shared dashboards to align promotions with inventory positioning. This integrated approach typically reduces stockouts by 20-30% while decreasing excess inventory costs."
+}
+```
+
+## Data Generation Methodology
+The data in this collection is synthetically generated. To ensure a high degree of quality and to mitigate single-model bias, a diverse set of state-of-the-art Large Language Models was used to generate portions of the dataset.
+Each model was prompted to generate responses that adhere strictly to the GSCF framework's principles and structure. The models used for generation include:
+
+- Sonnet 4.5
+- gpt-5
+- Mistral Medium 3
+- Kimi (K2)
+- Qwen 3 (235B)
+- Google Gemini 2.5 Pro
+- GLM-4.6
+- Deepseek R1
+
+This multi-model approach results in a robust and varied dataset that captures a broad range of linguistic styles while maintaining a consistent focus on the core GSCF methodology.
+
+## Intended Use
+This dataset is primarily intended for Supervised Fine-Tuning (SFT) of language models. It can be used to imbue a base model with the specialized knowledge and conversational patterns required to function as an expert GSCF consultant. The resulting fine-tuned model would be a valuable tool for business analysis, education, and decision support in the field of supply chain management.