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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- hu
+license: apache-2.0
+library_name: transformers
+base_model: utter-project/EuroLLM-1.7B
+pipeline_tag: text-generation
+tags:
+- hungarian
+- emese
+- eurollm
+- instruct
+- chatml
+- on-device
+---
+
+# Emese-Csermely (1.7B)
+
+**Csermely** ("brook") is the mobile / on-device tier of the [Emese](https://emese.tech) Hungarian model
+family — [EuroLLM-1.7B](https://huggingface.co/utter-project/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)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+tok = AutoTokenizer.from_pretrained("emese-tech/emese-csermely")
+model = AutoModelForCausalLM.from_pretrained("emese-tech/emese-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)
+
+```python
+from mlx_lm import load, generate
+from mlx_lm.sample_utils import make_sampler
+model, tok = load("emese-tech/emese-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.

chat_template.jinja

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+{% for message in messages %}<|im_start|>{{ message['role'] }}
+{{ message['content'] }}<|im_end|>
+{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "dtype": "bfloat16",
+    "eos_token_id": [
+        2,
+        4
+    ],
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 5632,
+    "max_position_embeddings": 4096,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 8,
+    "pad_token_id": null,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "rope_theta": 10000.0,
+        "rope_type": "default"
+    },
+    "tie_word_embeddings": false,
+    "transformers_version": "5.14.1",
+    "use_cache": true,
+    "vocab_size": 128000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2,
+    4
+  ],
+  "transformers_version": "5.14.1"
+}

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