初始化项目，由ModelHub XC社区提供模型

Model: ShikoShin/GPT2-Medium-MORIAI
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- openai-community/gpt2-medium
+---
+
+# MORI AI
+
+## Model Details
+
+### Model Description
+
+MORI (Machine-Oriented Responsive Intelligence) is a transformer-based conversational AI developed as part of the research project:
+
+**"Design and Implementation of an NLP-Driven Intelligent Web Companion for Real-Time User Interaction."**
+
+This version of MORI is specifically designed to assist students, faculty, and stakeholders of ICCT Colleges – San Mateo Campus by providing information related to academic programs, enrollment procedures, registrar services, school policies, and general campus inquiries.
+
+The model focuses on answering institution-related questions and providing guidance based on publicly available academic and administrative information.
+
+**Developed by:** ShikoShin
+**Model Type:** Transformer-Based Conversational AI
+**Language(s):** English
+**License:** Apache 2.0
+**Finetuned from:** DialoGPT-Large
+
+---
+
+# Model Sources
+
+**Institution:** ICCT Colleges
+
+**Research Project:** Design and Implementation of an NLP-Driven Intelligent Web Companion for Real-Time User Interaction
+
+---
+
+# Intended Use
+
+## Primary Use Cases
+
+MORI is intended to assist users with:
+
+* Academic program inquiries
+* Course information
+* Admission requirements
+* Enrollment procedures
+* Registrar-related concerns
+* School policies and guidelines
+* Frequently asked questions about ICCT Colleges – San Mateo Campus
+
+## Educational Support
+
+The model may be integrated into:
+
+* Student assistance platforms
+* School websites
+* Mobile applications
+* Information kiosks
+* Academic support systems
+
+---
+
+# Out-of-Scope Use
+
+MORI is not intended for:
+
+* Medical advice
+* Legal advice
+* Financial consulting
+* Psychological counseling
+* Emergency response
+* Safety-critical decision making
+
+Users should consult official ICCT personnel for authoritative decisions regarding academic records, enrollment status, and institutional policies.
+
+---
+
+# Training Data
+
+## Dataset Description
+
+MORI was trained using a curated dataset consisting of institution-specific information related to:
+
+* Academic courses and programs
+* Enrollment workflows
+* Admission requirements
+* Registrar procedures
+* Frequently asked student inquiries
+* Publicly available institutional information
+
+### Data Privacy Statement
+
+No sensitive personal information was used during training.
+
+The dataset does **not** contain:
+
+* Student records
+* Grades or transcripts
+* Personal identification information
+* Financial records
+* Medical information
+* Confidential institutional documents
+
+Training data was limited to educational and administrative information intended for public dissemination.
+
+---
+
+# Limitations
+
+MORI may:
+
+* Generate incorrect responses
+* Provide outdated information if institutional policies change
+* Misinterpret ambiguous questions
+* Require human verification for official transactions
+
+The model should be treated as an informational assistant rather than an official source of record.
+
+---
+
+# How to Get Started
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "ShikoShin/DialoGPT-Large-MORIAI"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "How do I enroll at ICCT San Mateo?"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.8
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+# Training Procedure
+
+## Preprocessing
+
+The dataset underwent:
+
+* Data cleaning
+* Duplicate removal
+* Text normalization
+* Formatting into question-answer pairs
+* Tokenization
+
+## Framework
+
+* Python
+* PyTorch
+* Hugging Face Transformers
+* Hugging Face Tokenizers
+
+---
+
+# Evaluation
+
+## Evaluation Criteria
+
+The model was evaluated based on:
+
+* Response relevance
+* Institutional information accuracy
+* Conversational consistency
+* User query understanding
+
+## Results
+
+MORI demonstrated the ability to answer common student and administrative questions related to ICCT Colleges – San Mateo Campus while maintaining conversational coherence.
+
+---
+
+# Technical Specifications
+
+## Architecture
+
+Transformer-based autoregressive language model fine-tuned from DialoGPT-Large.
+
+### Objective
+
+Generate contextually appropriate responses to institution-related inquiries while maintaining conversational flow.
+
+---
+
+# Citation
+
+### APA
+
+ShikoShin. (2026). MORI: Intelligent Academic Assistant for ICCT Colleges – San Mateo Campus. Hugging Face.
+
+### BibTeX
+
+@misc{mori2026,
+author = {ShikoShin},
+title = {MORI: Intelligent Academic Assistant for ICCT Colleges -- San Mateo Campus},
+year = {2026},
+publisher = {Hugging Face}
+}
+
+---
+
+# Authors
+
+ShikoShin
+
+---
+
+# Contact
+
+For questions, feedback, or collaboration opportunities, please contact the repository owner through Hugging Face.