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emese-tech/er-mlx

sourceHugging Facemitupdated 18d agoView on Hugging Face

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Model Card

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

python

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.