How to use khazarai/Medical-TTS with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/csm-1b") model = PeftModel.from_pretrained(base_model, "khazarai/Medical-TTS")
How to use khazarai/Medical-TTS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="khazarai/Medical-TTS")
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("khazarai/Medical-TTS", dtype="auto")
How to use khazarai/Medical-TTS with Unsloth Studio:
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 khazarai/Medical-TTS to start chatting
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 khazarai/Medical-TTS to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for khazarai/Medical-TTS to start chatting
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="khazarai/Medical-TTS", max_seq_length=2048, )
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