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

Libraries

How to use emese-tech/csermely with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("emese-tech/csermely")
model = AutoModelForCausalLM.from_pretrained("emese-tech/csermely", 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/csermely with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Emese-Csermely (1.7B)

Csermely ("brook") is the mobile / on-device tier of the Emese Hungarian model family — EuroLLM-1.7B continued-pretrained on Hungarian, instruction-tuned, and DPO-aligned, small enough to run fully offline on a phone.


Parameters	1.7B
Base	EuroLLM-1.7B
Architecture	LLaMA-style (RoPE θ=10,000, GQA 16Q/8KV, SwiGLU, RMSNorm)
Hidden / layers / heads	2048 / 24 / 16 (8 KV heads)
Vocabulary	128,000 (EuroLLM multilingual SentencePiece)
Max context length	4,096 tokens (EuroLLM-1.7B's native `max_position_embeddings` — unchanged by CPT/SFT/DPO, all of which trained at shorter sequence lengths of 1,024–2,048)
Precision	bfloat16
License	Apache-2.0

Formats in this release

Folder	Format	Size	Notes
`csermely/` (this repo)	bf16, standard HF `safetensors`	~3.1 GB	loads directly with `transformers` and `mlx_lm`
`csermely-mlx/`	MLX q8	~1.7 GB	`mlx_lm`-only (not `transformers`-loadable — see [PUBLISHING notes])

Usage (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("emese-tech/csermely")
model = AutoModelForCausalLM.from_pretrained("emese-tech/csermely", dtype=torch.bfloat16, device_map="auto")
msgs = [{"role": "user", "content": "Szia! Mit tudsz csinálni?"}]
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, on-device)

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
model, tok = load("emese-tech/csermely-mlx")  # q8
p = tok.apply_chat_template([{"role": "user", "content": "Szia! Mit tudsz csinálni?"}],
                            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 (chat_template.jinja ships in both repos). For multi-turn conversations, always pass the full history, not just the latest message.

Training

This is a ground-up redo (CPT + SFT + DPO) built via scripts/redo_pipeline.py, replacing the earlier v18b chain (still available internally as a fallback reference).

CPT — continued pretraining from raw EuroLLM-1.7B, mixing corpus/cpt/wiki.jsonl (Wikipedia) and corpus/cpt/hplt.jsonl (filtered web text) at a 65% wiki / 35% HPLT ratio — reversed from the 20/80 mix used for the larger tiers, to prioritize encyclopedic factual grounding over register diversity. LoRA rank 16 / scale 16 (no amplification), dropout 0.05, lr 1.5e-5, bottom 4 of 24 layers frozen as a grammar-forgetting guardrail. Known limitation (v1): this release's CPT ran at ~13% of the planned budget (300 of a planned 2,280 iterations, ~2M of a planned ~28M tokens) — a full-scale CPT pass is planned for a future release; see instruct/CSERMELY_REDO_PLAN.md.
SFT — full 1 epoch (1,228 iterations) on the shared 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 used for Patak and Folyó). LoRA rank 16 / scale 32, dropout 0.1, lr 2e-5, batch 4 × grad-accum 2. Checkpoint-swept across the training run; iteration 980 (~80% through training) was selected as the best-behaving checkpoint over the final (most-overfit) one.
DPO — 80 iterations of DPO-lite on 36 hand-written preference pairs (persona identity-defense + anti-repetition — the same pair bank used for Patak/Folyó's DPO). LoRA rank 8 / scale 8 (no amplification), lr 3e-6 — deliberately the softest DPO recipe in the family, scaled down for the smallest model's lower forgetting margin.

Benchmarks

	Ultimate Bench (0-250)	BlindSpot Bench (0-376)
This release	116/250 (46%)	137/376 (36%)
Previous shipped (`v18b`, SFT-only, no DPO)	118/250 (47%)	130/376 (35%)

Near-parity on Ultimate, a clean win on BlindSpot — despite the CPT stage still being at exercise scale. See archive/benchmarks/results/csermely-redo-full-{ULTIMATE,BLINDSPOT}.md for full category breakdowns.

emese-bench v1 (500 pts, consolidated Ultimate+BlindSpot, MLX q8): 211/500 (42%) — the current unified reference benchmark going forward. Strong on safety, factual basics, reading, and translation; weak on multi-step math (0/10), logic puzzles, structured output, and persona/identity (rarely says "Emese" when asked who it is). See emese-bench/results/csermely-mlx.md for the full category breakdown and emese-bench/README.md for the benchmark's design.

Limitations

Capacity-bound relative to Patak/Folyó: weaker at arithmetic, multi-step reasoning, and strict format-following (JSON/YAML/table output, exact word/sentence counts).
Confabulates on unanswerable/fictional-entity questions more often than the larger tiers.
Hungarian-first; other-language quality inherited from EuroLLM-1.7B.
CPT is under-scaled in this release (see Training notes above) — factual grounding may improve in a future release once CPT reaches its full planned token budget.

Downloads last month: 786

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for emese-tech/csermely

Base model

utter-project/EuroLLM-1.7B

Finetuned

(9)

this model