Instructions to use rajputsw/hindhi_pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajputsw/hindhi_pretrained with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rajputsw/hindhi_pretrained", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use rajputsw/hindhi_pretrained 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 rajputsw/hindhi_pretrained 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 rajputsw/hindhi_pretrained to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rajputsw/hindhi_pretrained to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rajputsw/hindhi_pretrained", max_seq_length=2048, )
Uploaded model
- Developed by: rajputsw
- License: apache-2.0
- Finetuned from model : kenpath/svara-tts-v1
This llama model was trained 2x faster with Unsloth
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Model tree for rajputsw/hindhi_pretrained
Base model
meta-llama/Llama-3.2-3B-Instruct Finetuned
canopylabs/orpheus-3b-0.1-pretrained Finetuned
canopylabs/3b-hi-ft-research_release Adapter
kenpath/svara-tts-v1
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rajputsw/hindhi_pretrained", dtype="auto")