Transformers
Safetensors
Italian
text-generation-inference
unsloth
llama
llama3.1
trl
word-game
rebus
italian
word-puzzle
crossword
Eval Results (legacy)
Instructions to use gsarti/llama-3.1-8b-rebus-solver-adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/llama-3.1-8b-rebus-solver-adapters with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gsarti/llama-3.1-8b-rebus-solver-adapters", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use gsarti/llama-3.1-8b-rebus-solver-adapters 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 gsarti/llama-3.1-8b-rebus-solver-adapters 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 gsarti/llama-3.1-8b-rebus-solver-adapters to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for gsarti/llama-3.1-8b-rebus-solver-adapters to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="gsarti/llama-3.1-8b-rebus-solver-adapters", max_seq_length=2048, )
- Xet hash:
- 458e3b5401816af79b89a82156421323ce80531aae97e9e5e3dae1774a8d2a65
- Size of remote file:
- 168 MB
- SHA256:
- 878992e9710cdfe9ddf3e46c8bb201134fdd6afc31ed9643590b39ab9094bca7
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