---
language:
- hu
license: apache-2.0
library_name: mlx
base_model: utter-project/EuroLLM-9B
pipeline_tag: text-generation
tags:
- hungarian
- emese
- eurollm
- instruct
- chatml
- mlx
- apple-silicon
- quantized
---

# 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.

| | |
|---|---|
| **Quantization** | q8, group size 64 |
| **Size on disk** | ~9.1 GB (vs. ~17 GB bf16) |
| **Quality** | This *is* the model's native precision — the `patak/` bf16 repo is dequantized *from* this, not the other way around. Own benchmark testing found bf16 and q8 score within 1 point of each other (218 vs. 217/250). |
| **Max context length** | 32,768 tokens (EuroLLM-9B's native context) |

## Usage

```python
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.