---
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](https://emese.tech)
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.