Instructions to use YFolla/gemma-3-dnd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use YFolla/gemma-3-dnd 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 YFolla/gemma-3-dnd 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 YFolla/gemma-3-dnd to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for YFolla/gemma-3-dnd to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="YFolla/gemma-3-dnd", max_seq_length=2048, )
Gemma-3-DnD (fine-tuned)
This model is a Gemma 3.88B parameter model fine-tuned specifically on Dungeons & Dragons content, including the Basic Rules and the Curse of Strahd campaign, using the Unsloth fine-tuning framework.
Model Details
- Base Model: Gemma-3-4b-it
- Fine-tuning: LoRA fine-tuning using Unsloth
- Content: D&D Basic Rules, Curse of Strahd, and related content.
Usage Example
You can load the model easily with the Hugging Face Transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch
model_name = "YFolla/gemma-3-dnd"
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "What are the core mechanics of Dungeons & Dragons?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
streamer = TextStreamer(tokenizer)
output = model.generate(**inputs, max_new_tokens=100, streamer=streamer)
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