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

Libraries

How to use emese-tech/patak with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("emese-tech/patak")
model = AutoModelForCausalLM.from_pretrained("emese-tech/patak", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use emese-tech/patak with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emese-tech/patak"
# 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/patak",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use emese-tech/patak 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/patak" \
    --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/patak",
		"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/patak" \
        --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/patak",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Emese-Patak (9.15B)

Patak ("stream") is the balanced, desktop-class tier of the Emese Hungarian model family — EuroLLM-9B continued-pretrained on Hungarian, instruction-tuned, and DPO-aligned. This is the family's best-benchmarked model.


Parameters	9.15B
Base	EuroLLM-9B
Architecture	LLaMA-style (RoPE θ=1,000,000, GQA 32Q/8KV, SwiGLU, RMSNorm)
Hidden / layers / heads	4096 / 42 / 32 (8 KV heads)
Vocabulary	128,000 (EuroLLM multilingual SentencePiece)
Max context length	32,768 tokens (EuroLLM-9B's native `max_position_embeddings`)
Precision	bfloat16 (this repo); MLX q8 also available (`patak-mlx/`)
License	Apache-2.0

Formats in this release

Folder	Format	Size	Notes
`patak/` (this repo)	bf16, standard HF `safetensors`	~17 GB	loads directly with `transformers` and `mlx_lm`; dequantized from the q8-native training chain (near-lossless, see Training)
`patak-mlx/`	MLX q8	~9.1 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/patak")
model = AutoModelForCausalLM.from_pretrained("emese-tech/patak", 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/patak-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 — light continued pretraining of EuroLLM-9B on the Emese Hungarian corpus: 5.1M tokens over 5,000 iterations (val loss 1.857). This is a known-light budget relative to the ~3.7B-token corpus available on disk (corpus/cpt/README.md) — a deeper CPT pass is a documented candidate for a future release (see instruct/V16_PATAK_CPT.md), but was not required to reach this release's benchmark result.
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). LoRA rank 16 / scale 32, dropout 0.1, lr 1.5e-5, gradient-checkpointed, all 42 layers + lm_head trained.
DPO — 120 iterations of DPO-lite ("alfa") on 36 hand-written preference pairs (persona identity-defense + anti-repetition), fixing residual persona/anti-repetition/honesty-calibration issues the SFT pass alone left behind. LoRA rank 16 / scale 32, lr 5e-6, trained on top of the quantized (q8) SFT model — this repo's bf16 weights are dequantized from that q8-native chain, which this project's own testing found near-lossless (MLX bf16 and q8 score within 1 point of each other on full benchmark runs).

Benchmarks

	Ultimate Bench (0-250)	BlindSpot Bench (0-376)
This release (DPO alfa)	218/250 (87%) — family all-time record	302/376

Safety: 100% refusal rate. Weak points: multi-turn refinement (revising an answer under a new constraint), occasional factual confabulation on obscure names/dates.

emese-bench v1 (500 pts, consolidated Ultimate+BlindSpot, MLX q8): 413/500 (83%) — by far the family's strongest result on the new unified benchmark. Near-perfect on longform, reading, code, safety, honesty, and English; the only real weak spots are multi-step math, spatial estimation, and code-debugging. See emese-bench/results/patak-mlx.md for the full category breakdown and emese-bench/README.md for the benchmark's design.

Limitations

Can hallucinate specific facts (dates, attributions) — verify critical details.
Multi-turn refinement is a known weak spot relative to single-turn quality.
Hungarian-first; other-language quality inherited from EuroLLM-9B.
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, regardless of source precision or quantization level, root-caused to llama.cpp's inference path itself (not this repo's weights). Use the transformers/mlx_lm paths above.

Downloads last month: 357

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for emese-tech/patak

Base model

utter-project/EuroLLM-9B

Finetuned

(9)

this model