Toots' MLX Quants
Collection
Various MLX quants for testing on Mac systems. • 51 items • Updated • 3
How to use mrtoots/unsloth-DeepSeek-V3.1-Terminus-mlx-2Bit with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mrtoots/unsloth-DeepSeek-V3.1-Terminus-mlx-2Bit", dtype="auto")How to use mrtoots/unsloth-DeepSeek-V3.1-Terminus-mlx-2Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir unsloth-DeepSeek-V3.1-Terminus-mlx-2Bit mrtoots/unsloth-DeepSeek-V3.1-Terminus-mlx-2Bit
The Model mrtoots/DeepSeek-V3.1-Terminus-mlx-2Bit was converted to MLX format from unsloth/DeepSeek-V3.1-Terminus using mlx-lm version 0.26.4.
This model was converted and quantized utilizing unsloth's version of DeepSeek-V3.1-Terminus.
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pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mrtoots/DeepSeek-V3.1-Terminus-mlx-2Bit")
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)
Quantized
Base model
deepseek-ai/DeepSeek-V3.1-Base