Instructions to use Jgdshkovi/csm-1b-lora-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jgdshkovi/csm-1b-lora-ft with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jgdshkovi/csm-1b-lora-ft", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Jgdshkovi/csm-1b-lora-ft 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 Jgdshkovi/csm-1b-lora-ft 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 Jgdshkovi/csm-1b-lora-ft to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Jgdshkovi/csm-1b-lora-ft to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Jgdshkovi/csm-1b-lora-ft", max_seq_length=2048, )
| { | |
| "chunk_length_s": null, | |
| "feature_extractor_type": "EncodecFeatureExtractor", | |
| "feature_size": 1, | |
| "overlap": null, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "CsmProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 24000 | |
| } | |