Text-to-Speech
Transformers
Safetensors
MLX
English
llama
text-generation
text-generation-inference
4-bit precision
Instructions to use nhe-ai/maya1-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nhe-ai/maya1-mlx-4Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="nhe-ai/maya1-mlx-4Bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nhe-ai/maya1-mlx-4Bit") model = AutoModelForCausalLM.from_pretrained("nhe-ai/maya1-mlx-4Bit") - MLX
How to use nhe-ai/maya1-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir maya1-mlx-4Bit nhe-ai/maya1-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
nhe-ai/maya1-mlx-4Bit
The Model nhe-ai/maya1-mlx-4Bit was converted to MLX format from maya-research/maya1 using mlx-lm version 0.26.4.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("nhe-ai/maya1-mlx-4Bit")
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
0.5B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
Model tree for nhe-ai/maya1-mlx-4Bit
Base model
maya-research/maya1