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
```

folyo / README.md

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	---
	language:
	- hu
	license: apache-2.0
	library_name: transformers
	base_model: utter-project/EuroLLM-22B
	pipeline_tag: text-generation
	tags:
	- hungarian
	- emese
	- eurollm
	- instruct
	- chatml
	---

	# Emese-Folyó (22B)

	Folyó ("river") is the flagship, server-class tier of the [Emese](https://emese.tech) Hungarian model
	family — [EuroLLM-22B](https://huggingface.co/utter-project/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)

	```python
	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)

	```python
	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.