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+emese-patak-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- hu
+license: apache-2.0
+library_name: gguf
+base_model: utter-project/EuroLLM-9B
+pipeline_tag: text-generation
+tags:
+- hungarian
+- emese
+- eurollm
+- instruct
+- chatml
+- gguf
+- llama.cpp
+- quantized
+---
+
+# Emese-Patak (9.15B) — GGUF Q4_K_M
+
+**GGUF Q4_K_M** — a compact `llama.cpp`-compatible build of Patak, quantized from the model's
+native q8 MLX artifact (`patak-mlx/`). See the `patak/` repo's README for full architecture,
+CPT/SFT/DPO training details, and benchmarks — this file covers only the GGUF-specific notes.
+
+| | |
+|---|---|
+| **Quantization** | Q4_K_M (llama.cpp k-quant) |
+| **Size on disk** | ~5.2 GB |
+| **Max context length** | 32,768 tokens (EuroLLM-9B's native context) |
+| **Runtime** | `llama.cpp` / `llama-server` / `llama-cli` / any GGUF-compatible loader (LM Studio, Ollama, etc.) |
+
+## ⚠️ Tokenizer fix required — read this before using any other GGUF build of this model
+
+A stock `convert_hf_to_gguf.py` export of this model family is **badly broken**: it types the
+ChatML control tokens (`<|im_start|>`, `<|im_end|>`) as `NORMAL` instead of `CONTROL`, so
+`<|im_start|>` gets shredded into 7 garbage sub-word tokens instead of being fed to the model as
+the single trained token — a prompt shape the model never saw during training. It also writes a
+flat placeholder BPE merge score for every token, corrupting subword-split priority. Together these
+caused a severe, previously-misdiagnosed quality regression (early testing wrongly concluded it was
+inherent to llama.cpp itself).
+
+**This GGUF file has already been fixed** — `scripts/fix_gguf_tokenizer.py` (in the main repo) was
+run on it after conversion/quantization to correct the special-token typing, BPE scores, and a
+stray leading-space flag. Verified: 80/80 sampled bench prompts tokenize byte-identical to the
+HF/MLX reference tokenization. **If you ever regenerate this GGUF from source yourself, you must
+re-run that fix script** (or the equivalent metadata patch) — a plain `convert_hf_to_gguf.py` +
+`llama-quantize` pipeline without it reproduces the old broken behavior.
+
+## Usage
+
+```bash
+llama-server -m emese-patak-Q4_K_M.gguf -c 4096
+```
+
+```python
+import requests
+r = requests.post("http://127.0.0.1:8080/v1/chat/completions", json={
+    "messages": [{"role": "user", "content": "Mi Magyarország fővárosa?"}],
+    "temperature": 0.2, "repeat_penalty": 1.15, "stop": ["<|im_end|>"],
+})
+print(r.json()["choices"][0]["message"]["content"])
+```
+
+**Decode:** temperature `0.2`, repeat_penalty `1.15`, stop on `<|im_end|>`, ChatML template
+(`<|im_start|>role\n...<|im_end|>\n`).
+
+## Training
+
+Same underlying weights as `patak-mlx/` (q8, the model's native training precision), just
+re-quantized to GGUF Q4_K_M — no separate training. See `patak/README.md` for the full CPT
+(5.1M tokens/5,000 iters), SFT (`instruct_v18b`, 1 epoch, rank16/scale32/lr1.5e-5), and DPO (36
+alfa pairs, 120 iters, rank16/scale32/lr5e-6) recipe.
+
+## Benchmarks
+
+**This exact Q4_K_M GGUF build (with the tokenizer fix applied) scored 384/500 (77%) on
+emese-bench v1**, vs. 391/500 (78%) for the same fix's Q8_0 build and 413/500 (83%) for the
+original MLX q8 artifact. Zero `<|im_start|>`/`<|im_end|>` leaks — the category-level strengths
+and weaknesses closely track the MLX result (near-perfect reading/translation/safety/code; weak
+on multi-step math and multi-constraint formatting). A residual, much smaller artifact distinct
+from the tokenizer bug was found in a couple of spots: the model's own completion occasionally
+leaks a fabricated literal `user` continuation into its response (a stop-condition timing issue,
+not a tokenizer problem). See `emese-bench/results/patak-gguf-q4fix.md` for the full
+category-by-category transcript and `emese-bench/README.md` for the benchmark's design and the
+Q8_0 comparison point.
+
+## Limitations
+
+- Can hallucinate specific facts (dates, attributions, biographical details) — verify critical
+  details. Two specific bench questions (about fictional/obscure Hungarian scientists) reliably
+  produce confidently-fabricated biographies across every tested variant of this model family.
+- Hungarian-first; other-language quality inherited from EuroLLM-9B.
+- Weak at multi-step math, spatial estimation, and strict multi-constraint formatting
+  (alphabetical ordering, exact word counts, banned letters) — consistent with the MLX original.

emese-patak-Q4_K_M.gguf

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