Instructions to use emese-tech/folyo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use emese-tech/folyo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="emese-tech/folyo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("emese-tech/folyo")
model = AutoModelForCausalLM.from_pretrained("emese-tech/folyo", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use emese-tech/folyo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emese-tech/folyo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emese-tech/folyo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/emese-tech/folyo

SGLang

How to use emese-tech/folyo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "emese-tech/folyo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emese-tech/folyo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "emese-tech/folyo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emese-tech/folyo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use emese-tech/folyo with Docker Model Runner:
```
docker model run hf.co/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.


Parameters	22B
Base	EuroLLM-22B
Architecture	LLaMA-style (RoPE θ=1,000,000, GQA 48Q/8KV, SwiGLU, RMSNorm)
Hidden / layers / heads	6144 / 54 / 48 (8 KV heads)
Vocabulary	128,000 (EuroLLM multilingual SentencePiece)
Max context length	32,768 tokens (EuroLLM-22B's native `max_position_embeddings`)
Precision	bfloat16 (this repo, dequantized for `transformers` compatibility); MLX q8 also available (`folyo-mlx/`)
License	Apache-2.0

Formats in this release

Folder	Format	Size	Notes
`folyo/` (this repo)	bf16, standard HF `safetensors`	~42 GB	loads directly with `transformers` and `mlx_lm`; dequantized from the q8-native training chain (near-lossless)
`folyo-mlx/`	MLX q8	~22 GB	`mlx_lm`-only, the native training/serving precision for this model

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

	Ultimate Bench (0-250)	BlindSpot Bench (0-376)
This release (DPO alfa)	211/250 (84%)	310/376 — family-best BlindSpot score

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.

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Safetensors

Model size

23B params

Tensor type

BF16

Model tree for emese-tech/folyo

Base model

utter-project/EuroLLM-22B-2512

Finetuned

(4)

this model