Instructions to use arcee-ai/Arcee-Maestro-7B-Preview-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcee-ai/Arcee-Maestro-7B-Preview-MLX with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("arcee-ai/Arcee-Maestro-7B-Preview-MLX", dtype="auto") - MLX
How to use arcee-ai/Arcee-Maestro-7B-Preview-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Arcee-Maestro-7B-Preview-MLX arcee-ai/Arcee-Maestro-7B-Preview-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
arcee-ai/Arcee-Maestro-7B-Preview-MLX
The Model arcee-ai/Arcee-Maestro-7B-Preview-MLX was converted to MLX format from arcee-ai/Arcee-Maestro-7B-Preview using mlx-lm version 0.21.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="arcee-ai/Arcee-Maestro-7B-Preview-MLX",
allow_patterns="4bit/*", # This will download everything in the 4bit folder
local_dir="Arcee-Maestro-7B-Preview-4bit" # Optional: specify where to save
)
model, tokenizer = load("Arcee-Maestro-7B-Preview-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
Hardware compatibility
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Model tree for arcee-ai/Arcee-Maestro-7B-Preview-MLX
Base model
deepseek-ai/DeepSeek-R1-Distill-Qwen-7B Finetuned
arcee-ai/Arcee-Maestro-7B-Preview