Instructions to use lilgoose777/lora_model_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilgoose777/lora_model_finetuned with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lilgoose777/lora_model_finetuned", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use lilgoose777/lora_model_finetuned 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 lilgoose777/lora_model_finetuned 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 lilgoose777/lora_model_finetuned to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lilgoose777/lora_model_finetuned to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="lilgoose777/lora_model_finetuned", max_seq_length=2048, )
File size: 356 Bytes
1097319 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"chunk_length": 30,
"dither": 0.0,
"feature_extractor_type": "WhisperFeatureExtractor",
"feature_size": 128,
"hop_length": 160,
"n_fft": 400,
"n_samples": 480000,
"nb_max_frames": 3000,
"padding_side": "left",
"padding_value": 0.0,
"processor_class": "WhisperProcessor",
"return_attention_mask": false,
"sampling_rate": 16000
}
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