Instructions to use bobig/orpheus-finetuned-3b-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bobig/orpheus-finetuned-3b-mlx-8Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="bobig/orpheus-finetuned-3b-mlx-8Bit")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bobig/orpheus-finetuned-3b-mlx-8Bit", dtype="auto") - MLX
How to use bobig/orpheus-finetuned-3b-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir orpheus-finetuned-3b-mlx-8Bit bobig/orpheus-finetuned-3b-mlx-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
bobig/orpheus-finetuned-3b-mlx-8Bit
better than the Q6, but still generating garbage tokens.
The Model bobig/orpheus-finetuned-3b-mlx-8Bit was converted to MLX format from srinivasbilla/orpheus-finetuned-3b using mlx-lm version 0.26.4.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("bobig/orpheus-finetuned-3b-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
0.9B params
Tensor type
F32
·
U32 ·
Hardware compatibility
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Model tree for bobig/orpheus-finetuned-3b-mlx-8Bit
Base model
meta-llama/Llama-3.2-3B-Instruct Finetuned
canopylabs/orpheus-3b-0.1-pretrained Finetuned
srinivasbilla/orpheus-finetuned-3b