Instructions to use Fmuaddib/gemma-3-27b-it-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fmuaddib/gemma-3-27b-it-mlx-8Bit with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Fmuaddib/gemma-3-27b-it-mlx-8Bit", dtype="auto") - MLX
How to use Fmuaddib/gemma-3-27b-it-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gemma-3-27b-it-mlx-8Bit Fmuaddib/gemma-3-27b-it-mlx-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Unsloth Studio
How to use Fmuaddib/gemma-3-27b-it-mlx-8Bit with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Fmuaddib/gemma-3-27b-it-mlx-8Bit to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Fmuaddib/gemma-3-27b-it-mlx-8Bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Fmuaddib/gemma-3-27b-it-mlx-8Bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Fmuaddib/gemma-3-27b-it-mlx-8Bit", max_seq_length=2048, )
Fmuaddib/gemma-3-27b-it-mlx-8Bit
The Model Fmuaddib/gemma-3-27b-it-mlx-8Bit was converted to MLX format from unsloth/gemma-3-27b-it using mlx-lm version 0.22.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Fmuaddib/gemma-3-27b-it-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
8B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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