Gemma3-Callous-Calla-4B — MLX builds (Apple Silicon)
This repo hosts MLX-converted variants of Daizee/Gemma3-Callous-Calla-4B for fast, local inference on Apple Silicon (M-series).
Tokenizer/config are included at the repo root. MLX weight folders live under mlx/.
Note on vocab padding: For MLX compatibility, the tokenizer/embeddings were padded to the next multiple of 64 tokens.
In this build: 262,208 tokens (added 64 placeholder tokens named<pad_ex_*>).
Variants
| Path | Bits | Group Size | Notes |
|---|---|---|---|
mlx/g128/ |
int4 | 128 | Smallest & fastest |
mlx/g64/ |
int4 | 64 | Slightly larger, often steadier |
mlx/int8/ |
8 | — | Closest to fp16 quality (slower) |
Quickstart (MLX-LM)
Run from Hugging Face (no cloning needed)
python -m mlx_lm.generate \
--model hf://Daizee/Gemma3-Callous-Calla-4B-mlx/mlx/g64 \
--prompt "Summarize the Bill of Rights for 7th graders in 4 bullet points." \
--max-tokens 180 --temp 0.3 --top-p 0.92
- Downloads last month
- 11
Hardware compatibility
Log In to add your hardware
Quantized
Model tree for Daizee/Gemma3-Callous-Calla-4B-mlx
Base model
Daizee/Gemma3-Callous-Calla-4B
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Daizee/Gemma3-Callous-Calla-4B-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True)