LoRA adapters and TensorBoard logs for Hypa-Orpheus 3B TTS/VC.

This repository is the LoRA adapter companion to hypaai/Hypa-Orpheus-3b-TTS-VC.

Both models belong to the Hypa-Orpheus collection and describe the same Step 3a production fine-tune. The only difference is what is stored here:

Repository	Contents	Best for
`hypaai/Hypa-Orpheus-3b-TTS-VC`	Merged 16-bit weights	Inference, vLLM serving, production deployment
`hypaai/Hypa-Orpheus-3b-TTS-VC-LoRAs` (this repo)	LoRA adapters + TensorBoard logs	Continued fine-tuning, adapter inspection, training metrics

For the full model description, capabilities, prompt format, languages, training data, hyperparameters, evaluation, limitations, citation, and acknowledgements, see the main README:

hypaai/Hypa-Orpheus-3b-TTS-VC README

Adapter at a Glance

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

TensorBoard

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.

Quick Usage

Load adapters for continued fine-tuning (Unsloth)

from unsloth import FastLanguageModel

model, 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 weights
from peft import PeftModel
model = PeftModel.from_pretrained(model, "hypaai/Hypa-Orpheus-3b-TTS-VC-LoRAs")

Merge to 16-bit for inference

After loading the adapter, merge into full weights for deployment (same artifact as the sibling repo):

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

Repository Contents

Hypa-Orpheus-3b-TTS-VC-LoRAs/
├── README.md
├── adapter_config.json
├── adapter_model.safetensors
├── training_args.bin
└── runs/
    └── events.out.tfevents.*   # TensorBoard scalars