Text Generation
Transformers
Safetensors
English
phi
study-assistant
emotionally-intelligent
casual-ai
text-generation-inference
Instructions to use redmint/studybuddyv2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redmint/studybuddyv2.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="redmint/studybuddyv2.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("redmint/studybuddyv2.1") model = AutoModelForCausalLM.from_pretrained("redmint/studybuddyv2.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use redmint/studybuddyv2.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "redmint/studybuddyv2.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "redmint/studybuddyv2.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/redmint/studybuddyv2.1
- SGLang
How to use redmint/studybuddyv2.1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "redmint/studybuddyv2.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "redmint/studybuddyv2.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "redmint/studybuddyv2.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "redmint/studybuddyv2.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use redmint/studybuddyv2.1 with Docker Model Runner:
docker model run hf.co/redmint/studybuddyv2.1
| license: cc-by-nc-4.0 | |
| tags: | |
| - text-generation | |
| - study-assistant | |
| - emotionally-intelligent | |
| - casual-ai | |
| inference: true | |
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| widget: | |
| - text: Can you help me study math? | |
| base_model: | |
| - microsoft/phi-2 | |
| --- | |
| base_model: microsoft/phi-2 | |
| library_name: peft | |
| tags: | |
| - study-assistant | |
| - education | |
| - phi-2 | |
| - fine-tuned | |
| - emotional-intelligence | |
| license: mit | |
| inference: true | |
| --- | |
| # Study Buddy AI v2.1 | |
| A fine-tuned version of Microsoft's Phi-2, trained to stay faithful to retrieved data and think in chains of thought. A study assistant that helps users learn deeply through conversation. Designed for real-time interaction inside a lightweight web app. | |
|  <!-- Replace this with your logo when ready --> | |
| ## Model Details: | |
| ### Model Description | |
| Study Buddy AI is built on `microsoft/phi-2` and fine-tuned using PEFT (Parameter-Efficient Fine-Tuning). It was trained to behave like a study partner β casual, reasoning, encouraging, and deeply helpful for academic subjects and personal growth. | |
| ### Features | |
| - Friendly tone tailored for Gen Z and students | |
| - Explains complex topics using simple language | |
| - Emotionally intelligent responses β supportive and motivational | |
| - Great for studying, reviewing, or breaking down hard concepts | |
| ### Use Cases | |
| - Personal tutoring via chat | |
| - Homework help and study review | |
| - Emotional motivation during learning sessions | |
| - Supplementing lessons with a relatable assistant | |
| ### Limitations | |
| - Best used in short back-and-forths, not long essays | |
| ## Training & Fine-Tuning | |
| - **Base model**: `microsoft/phi-2` | |
| - **Fine-tuning method**: LoRA via the `peft` library | |
| - **Custom dataset**: 3,000+ curated prompts focused on simplified teaching, multi-step thinking, and staying loyal to retrieved data. | |
| - **Hosted via**: Hugging Face Inference Endpoints (GPU-backed) | |
| ## How It Works | |
| Frontend β Flask API β Hugging Face Model β Response β KnowBetter Logic (to clean output) | |
| Built to run inside a React + Flask stack with CORS enabled and token-secured backend access to Hugging Face. | |
| ## Model Creator | |
| **Creator**: Micah Miles | |
| **GitHub/Contact**: redmint-him | |
| **Team**: Redmint | |
| **Project**: Study Buddy AI | |
| ## License | |
| This model is licensed under the **Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)**. | |
| You are free to: | |
| - Use the model for **personal or educational purposes** | |
| - Modify, remix, or build upon it | |
| **You may not**: | |
| - Sell, sublicense, or use the model or its outputs for any commercial purposes | |
| Β© 2025 Micah Miles | |
| ## NOTICE | |
| This model is licensed for **non-commercial use only**. | |
| For commercial licensing or partnership inquiries, contact: micahmiles21@gmail.com | |
| --- | |
| ## "IF YOU'RE READING THIS IT'S NOT TOO LATE." | |
| ## "If man can, I can, You can, We can." | |
| ## ~ MICAH MILES. |