Instructions to use blah7/lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blah7/lora_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="blah7/lora_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("blah7/lora_model") model = AutoModelForTextToWaveform.from_pretrained("blah7/lora_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
|
Download README.md from blah7/lora_model: direct link, hf CLI and curl.
- Browser
- Download file 546 Bytes
-
https://huggingface.co/blah7/lora_model/resolve/main/README.md
- Command line
-
hf download hf://blah7/lora_model/README.md
-
curl -L -o README.md https://huggingface.co/blah7/lora_model/resolve/main/README.md
546 Bytes
metadata
base_model: unsloth/csm-1b
tags:
- text-generation-inference
- transformers
- unsloth
- csm
license: apache-2.0
language:
- en
Uploaded finetuned model
- Developed by: blah7
- License: apache-2.0
- Finetuned from model : unsloth/csm-1b
This csm model was trained 2x faster with Unsloth and Huggingface's TRL library.
