Trinity-Nano-Preview-oQ
Collection
3 items • Updated • 1
oQ4 mixed-precision MLX quantization produced via oMLX.
from mlx_lm import load, generate
model, tokenizer = load("bearzi/Trinity-Nano-Preview-oQ4")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "Hello"}],
add_generation_prompt=True,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512, verbose=True))
oQ measures per-layer quantization sensitivity through calibration and allocates bits where they matter most — critical layers stay at higher precision, tolerant layers compress aggressively. Target averages of 2/3/4/6/8 bits are provided; actual per-layer bits vary by measured sensitivity.
See oQ documentation.
Comparative benchmarks and feedback welcome — please open a discussion.
4-bit
Base model
arcee-ai/Trinity-Nano-Base-Pre-Anneal