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Model: ShikoShin/GPT2-Medium-MORIAI Source: Original Platform
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README.md
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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base_model:
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- openai-community/gpt2-medium
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---
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# MORI AI
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## Model Details
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### Model Description
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MORI (Machine-Oriented Responsive Intelligence) is a transformer-based conversational AI developed as part of the research project:
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**"Design and Implementation of an NLP-Driven Intelligent Web Companion for Real-Time User Interaction."**
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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.
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The model focuses on answering institution-related questions and providing guidance based on publicly available academic and administrative information.
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**Developed by:** ShikoShin
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**Model Type:** Transformer-Based Conversational AI
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**Language(s):** English
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**License:** Apache 2.0
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**Finetuned from:** DialoGPT-Large
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---
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# Model Sources
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**Institution:** ICCT Colleges
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**Research Project:** Design and Implementation of an NLP-Driven Intelligent Web Companion for Real-Time User Interaction
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---
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# Intended Use
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## Primary Use Cases
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MORI is intended to assist users with:
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* Academic program inquiries
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* Course information
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* Admission requirements
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* Enrollment procedures
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* Registrar-related concerns
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* School policies and guidelines
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* Frequently asked questions about ICCT Colleges – San Mateo Campus
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## Educational Support
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The model may be integrated into:
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* Student assistance platforms
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* School websites
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* Mobile applications
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* Information kiosks
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* Academic support systems
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---
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# Out-of-Scope Use
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MORI is not intended for:
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* Medical advice
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* Legal advice
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* Financial consulting
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* Psychological counseling
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* Emergency response
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* Safety-critical decision making
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Users should consult official ICCT personnel for authoritative decisions regarding academic records, enrollment status, and institutional policies.
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---
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# Training Data
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## Dataset Description
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MORI was trained using a curated dataset consisting of institution-specific information related to:
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* Academic courses and programs
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* Enrollment workflows
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* Admission requirements
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* Registrar procedures
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* Frequently asked student inquiries
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* Publicly available institutional information
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### Data Privacy Statement
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No sensitive personal information was used during training.
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The dataset does **not** contain:
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* Student records
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* Grades or transcripts
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* Personal identification information
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* Financial records
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* Medical information
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* Confidential institutional documents
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Training data was limited to educational and administrative information intended for public dissemination.
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---
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# Limitations
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MORI may:
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* Generate incorrect responses
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* Provide outdated information if institutional policies change
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* Misinterpret ambiguous questions
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* Require human verification for official transactions
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The model should be treated as an informational assistant rather than an official source of record.
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---
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# How to Get Started
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "ShikoShin/DialoGPT-Large-MORIAI"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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prompt = "How do I enroll at ICCT San Mateo?"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(
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**inputs,
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max_new_tokens=100,
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do_sample=True,
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temperature=0.8
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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# Training Procedure
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## Preprocessing
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The dataset underwent:
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* Data cleaning
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* Duplicate removal
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* Text normalization
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* Formatting into question-answer pairs
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* Tokenization
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## Framework
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* Python
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* PyTorch
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* Hugging Face Transformers
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* Hugging Face Tokenizers
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---
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# Evaluation
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## Evaluation Criteria
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The model was evaluated based on:
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* Response relevance
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* Institutional information accuracy
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* Conversational consistency
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* User query understanding
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## Results
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MORI demonstrated the ability to answer common student and administrative questions related to ICCT Colleges – San Mateo Campus while maintaining conversational coherence.
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---
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# Technical Specifications
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## Architecture
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Transformer-based autoregressive language model fine-tuned from DialoGPT-Large.
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### Objective
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Generate contextually appropriate responses to institution-related inquiries while maintaining conversational flow.
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---
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# Citation
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### APA
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ShikoShin. (2026). MORI: Intelligent Academic Assistant for ICCT Colleges – San Mateo Campus. Hugging Face.
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### BibTeX
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@misc{mori2026,
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author = {ShikoShin},
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title = {MORI: Intelligent Academic Assistant for ICCT Colleges -- San Mateo Campus},
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year = {2026},
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publisher = {Hugging Face}
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}
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---
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# Authors
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ShikoShin
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---
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# Contact
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For questions, feedback, or collaboration opportunities, please contact the repository owner through Hugging Face.
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chat_template.jinja
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{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}
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config.json
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{
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"activation_function": "gelu_new",
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"add_cross_attention": false,
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"dtype": "float32",
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_embd": 1024,
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"n_head": 16,
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"n_inner": null,
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"n_layer": 24,
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"n_positions": 1024,
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"n_special": 0,
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"pad_token_id": 50256,
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"predict_special_tokens": true,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50
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}
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0",
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"use_cache": false,
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"vocab_size": 50259
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"transformers_version": "5.5.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:eed4e77e976eda0f467b6eda80379cfa55e810c007e9a617ae3c2c815db37bfd
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size 1419331072
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250338
tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<user>",
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"<mori>"
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],
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"is_local": true,
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"padding_side": "right",
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"stride": 0,
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"tokenizer_class": "GPT2Tokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<|endoftext|>"
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}
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