Instructions to use fklska/school-answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use fklska/school-answer with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for fklska/school-answer to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for fklska/school-answer to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for fklska/school-answer to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="fklska/school-answer", max_seq_length=2048, )
| tags: | |
| - gguf | |
| - llama.cpp | |
| - unsloth | |
| # school-answer : GGUF | |
| This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth). | |
| **Example usage**: | |
| - For text only LLMs: `llama-cli -hf fklska/school-answer --jinja` | |
| - For multimodal models: `llama-mtmd-cli -hf fklska/school-answer --jinja` | |
| ## Available Model files: | |
| - `qwen3-4b-instruct-2507.Q4_K_M.gguf` | |
| ## Ollama | |
| An Ollama Modelfile is included for easy deployment. | |
| This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) | |
| [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | |