emese-tech / folyo

Emese-Folyó (22B)

Folyó ("river") is the flagship, server-class tier of the Emese Hungarian model family — EuroLLM-22B continued-pretrained on Hungarian, instruction-tuned, and DPO-aligned.

Formats in this release

Usage (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("emese-tech/folyo")
model = AutoModelForCausalLM.from_pretrained("emese-tech/folyo", dtype=torch.bfloat16, device_map="auto")
msgs = [{"role": "user", "content": "Mi Magyarország fővárosa?"}]
prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
ids = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=256, do_sample=True, temperature=0.2, eos_token_id=[2, 4])
print(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True))

Usage (MLX)

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
model, tok = load("emese-tech/folyo-mlx")  # q8
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} ( </s> and <|im_end|> — omitting id 4 causes non-stopping generation), ChatML template. For multi-turn conversations, always pass the full history , not just the latest message.

Training

• CPT — q8-native continued pretraining of EuroLLM-22B on the Emese Hungarian corpus: ~6M tokens over 6,000 iterations , LoRA rank 64, then fused and requantized to q8 (rather than kept in bf16) to avoid a q4→bf16 fuse-precision mismatch discovered earlier in this project's history. The resulting q8-native CPT base ( eurollm-22b-cpt-v2-q8 ) is what every downstream SFT/DPO stage built on.
• SFT — 1 epoch (4,914 iterations) on the instruct_v18b corpus (4,914 rows: persona, safety, code + code-debug, hedging/anti-confabulation, multi-step reasoning, compound constraints, multi-turn refinement, anti-repetition — same corpus as Patak/Csermely). LoRA rank 16 / scale 16, dropout 0.1, lr 5e-6, bottom 14 of 54 layers frozen ( num_layers: 40 ). Checkpoint-swept across the run; iteration 3,600 (not the final iteration) was selected as the best-behaving checkpoint.
• DPO — 120 iterations of DPO-lite ("alfa") on 36 hand-written preference pairs (persona identity-defense + anti-repetition — same pair bank as Patak/Csermely). LoRA rank 16 / scale 16, lr 2e-6 — the lowest DPO learning rate in the family, since this checkpoint was already the peak-swept best and had the least margin to spare.

Benchmarks

emese-bench v1 (500 pts, consolidated Ultimate+BlindSpot, MLX q8): 410/500 (82%) — the current unified reference benchmark going forward, just behind Patak (413/500) and far ahead of Csermely (211/500). Near-perfect on safety, safety_edge, honesty_calibration, persona_selfaware, reading, translation, and code-writing, with no repetition loops or stop-token leakage anywhere in the transcript. Weakest on multi-step math, logic puzzles, and two confidently-fabricated Hungarian scientist biographies; the standout chat-session finding is a perfect 10/10 on both in-context memory recall and roleplay (best in the family so far), offset by a surprising 1/10 on the English-chat session — the model answered every turn in Hungarian despite the user writing in English. See emese-bench/results/folyo-mlx.md and emese-bench/README.md for full category-level detail.

Limitations

• Can hallucinate specific facts (dates, attributions) — verify critical details.
• Hungarian-first; other-language quality inherited from EuroLLM-22B.
• Large — 42 GB bf16 / 22 GB q8; needs a machine with enough RAM/VRAM for practical serving.
• Do not convert this model to GGUF without re-validating first — this project's own testing found a consistent ~14-27 point Ultimate Bench regression when serving via llama.cpp/GGUF (tested on Patak, same architecture family), regardless of source precision or quantization level, root-caused to llama.cpp's inference path itself. Use the transformers / mlx_lm paths above.