emese-tech / patak-mlx

Emese-Patak (9.15B) — MLX q8

MLX q8 — the native training/serving precision for Patak (SFT and DPO were both trained directly on top of a q8-quantized base, not fused-then-quantized after the fact). See the patak/ repo's README for full architecture, CPT/SFT/DPO training details, and benchmarks — this file covers only the q8-specific notes.

Usage

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
model, tok = load("emese-tech/patak-mlx")
p = tok.apply_chat_template([{"role": "user", "content": "Mi Magyarország fővárosa?"}],
                            tokenize=False, add_generation_prompt=True)
print(generate(model, tok, prompt=p, max_tokens=256, sampler=make_sampler(temp=0.2)))

Decode: temperature 0.2 , no repetition penalty, eos {2, 4} , ChatML template.

⚠️ This repo is mlx_lm -only — MLX's q8 quantization packs weights into uint32 + per-group scales / biases tensors with a quantization block in config.json that plain transformers does not understand. Use the patak/ (bf16) repo for transformers /vLLM/TGI.

Training

This is the primary artifact of the SFT+DPO training chain — see patak/README.md for the full CPT (5.1M tokens/5,000 iters), SFT ( instruct_v18b , 1 epoch, rank16/scale32/lr1.5e-5), and DPO (36 alfa pairs, 120 iters, rank16/scale32/lr5e-6) recipe, plus the 218/250 Ultimate · 302/376 BlindSpot benchmark results.

Benchmarks

This exact q8 artifact scored 413/500 (83%) on emese-bench v1 (the consolidated 500-pt Ultimate+BlindSpot benchmark) — by far the family's strongest result, near-perfect on longform, reading, code, safety, honesty, and English. See emese-bench/results/patak-mlx.md for the full category-by-category transcript and emese-bench/README.md for the benchmark's design.