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Model Details
Model Provider
hypaai
Model Tree
unsloth/orpheus-3b-0.1-ft
Input Modalities
Output Modalities
Supported Functionality
| Item | Value |
|---|---|
| Base model | unsloth/orpheus-3b-0.1-ft |
| Method | 4-bit QLoRA via Unsloth |
| LoRA rank / alpha | r=512, α=512 |
| Trainable parameters | 778M / 4.08B (19.07%) |
| Shipped checkpoint | Step 44,334 (validation-best) |
| Best validation loss | 3.513 @ step 44,334 |
| License | Apache 2.0 |
Interactive training logs for the production run:
View TensorBoard on Hugging Face
Headline metrics from the shipped run:
| Metric | Value | Step |
|---|---|---|
| Initial training loss | 4.743 | 1 |
| Best training loss | 3.549 | 26,604 |
| Final training loss | 3.560 | 42,123 |
| Initial validation loss | 3.870 | 2,217 |
| Best validation loss (shipped) | 3.513 | 44,334 |
Validation loss improved at every evaluation interval. The merged release in the sibling repository uses this validation-best checkpoint.
python
from unsloth import FastLanguageModelmodel, tokenizer = FastLanguageModel.from_pretrained(model_name="unsloth/orpheus-3b-0.1-ft",max_seq_length=2048,dtype=None,load_in_4bit=True,)model = FastLanguageModel.get_peft_model(model,r=512,lora_alpha=512,target_modules=["q_proj", "k_proj", "v_proj", "o_proj","gate_proj", "up_proj", "down_proj",],lora_dropout=0.0,bias="none",use_gradient_checkpointing="unsloth",)# Load the shipped Hypa-Orpheus adapter weightsfrom peft import PeftModelmodel = PeftModel.from_pretrained(model, "hypaai/Hypa-Orpheus-3b-TTS-VC-LoRAs")
After loading the adapter, merge into full weights for deployment (same artifact as the sibling repo):
python
model = model.merge_and_unload()model.save_pretrained("Hypa-Orpheus-3b-TTS-VC-merged")tokenizer.save_pretrained("Hypa-Orpheus-3b-TTS-VC-merged")
For production inference, you can also use the pre-merged checkpoint directly:
hypaai/Hypa-Orpheus-3b-TTS-VC
markdown
Hypa-Orpheus-3b-TTS-VC-LoRAs/├── README.md├── adapter_config.json├── adapter_model.safetensors├── training_args.bin└── runs/└── events.out.tfevents.* # TensorBoard scalars
For questions or contributions, contact chris@hypaintelligence.com or open an issue on Hugging Face.