Instructions to use Dhahlan2000/sin_en_combined_to_tamil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhahlan2000/sin_en_combined_to_tamil with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dhahlan2000/sin_en_combined_to_tamil", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Dhahlan2000/sin_en_combined_to_tamil 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 Dhahlan2000/sin_en_combined_to_tamil 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 Dhahlan2000/sin_en_combined_to_tamil to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Dhahlan2000/sin_en_combined_to_tamil to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Dhahlan2000/sin_en_combined_to_tamil", max_seq_length=2048, )
Uploaded model
- Developed by: Dhahlan2000
- License: apache-2.0
- Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
1 1.188400 2 1.097600 3 1.152100 4 1.101800 5 1.143300 6 0.932700 7 0.904900 8 0.856600 9 0.859000 10 0.685300 11 0.722800 12 0.673000 13 0.679200 14 0.683700 15 0.671200 16 0.545200 17 0.668600 18 0.577200 19 0.648000 20 0.600600 21 0.571200 22 0.596500 23 0.578400 24 0.596400 25 0.614000 26 0.646500 27 0.564300 28 0.587800 29 0.588200 30 0.522800 31 0.557200 32 0.539400 33 0.615300 34 0.610300 35 0.548500 36 0.593800 37 0.599300 38 0.535000 39 0.573200 40 0.616500 41 0.576300 42 0.615900 43 0.508700 44 0.559700 45 0.577500 46 0.536300 47 0.648800 48 0.580000 49 0.570700 50 0.496800 51 0.542300 52 0.566900 53 0.533700 54 0.508400 55 0.635700 56 0.554000 57 0.565900 58 0.539500 59 0.598300 60 0.518600
Input:
සඳුදා දින, Stanford University වෛද්ය පාසලේ විද්යාඥයෝ සෛල type අනුව sort කරන්න පුළුවන් නව diagnostic tool එකක්—හෙවත් standard inkjet printers වලින් print කරන්න පුළුවන් tiny chip එකක්—සොයාගත් බව නිවේදනය කළා.
Response:
சனிக்குத்தான், ஸ்டான்ஃபோர்ட் பல்கலைக்கழகத்தின் மருத்துவப் பள்ளியில் உயிரணுக்களை இனங்களாகப் பிரிக்க முடியும் ஒரு புதிய முறையைக் கண்டுபிடித்தனர், அதாவது ஒரு பாரம்பரிய இன்ஜெட் பிரிண்டர் போன்ற சிறிய சிப்பாயில் இயந்திரம்.<|end_of_text|>
