language:
- hu
license: mit
library_name: mlx
tags:
- hungarian
- emese
- from-scratch
- llama
- base-model
- mlx
- apple-silicon
- quantized
pipeline_tag: text-generation
Emese-Ér (517M) — MLX q8
MLX q8-quantized build of Ér, the edge/embedded/research tier of the Emese
Hungarian model family — a from-scratch 517M-parameter foundation model (not EuroLLM-based). See the
er/ repo's README for full architecture, training, and limitations details; this file only covers the
q8-specific notes.
| Quantization | q8, group size 64 (mlx_lm convert -q --q-bits 8 --q-group-size 64) |
| Size on disk | ~526 MB (vs. ~987 MB bf16) |
| Quality | ≈ bf16, near-lossless at 8-bit |
| Max context length | 32,768 tokens (unchanged from bf16 — quantization doesn't affect context) |
Usage
from mlx_lm import load, generate
model, tok = load("er-mlx")
print(generate(model, tok, prompt="A magyar nyelv", max_tokens=100))
Base model only — no chat template. This checkpoint loads via mlx_lm despite its non-standard
config.json schema (see the er/ README for why plain transformers can't load either variant).
Training
Identical underlying weights to er/ (bf16), just quantized after the fact — no separate training. See
er/README.md for the full from-scratch pretraining details (~4.5B Hungarian tokens, no SFT/DPO).
Benchmarks
Not applicable — see er/README.md: Ér has no chat template or instruction-tuning, so the
chat-instruction benchmarks used for the other three tiers (emese-bench) don't apply.