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Update SL modelcard v1.13 — full translation

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@@ -6,14 +6,14 @@ library_name: transformers
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  tags: [balkan, southeast-europe, multilingual, honest-ai, sovasoft, rag]
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  ---
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- > **🌐 This is the Slovenian (Slovenščina) Model Card.** The body text remains in English for international readability.
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11
 
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  <p align="center">
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- <img src="images/Zora_Stich_1700_SW_Print.png" width="520" alt="Zora — Goddess of Dawn, surrounded by the symbols of 12 peoples"/>
14
  </p>
15
 
16
- # Zora v1.13 — odprti, iskren LLM za Balkan in Jugo Evropo
17
 
18
  *зора = "zora". Ena da jih vse združi.* — **Sovasoft** ([ai.in.rs](https://ai.in.rs))
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@@ -21,195 +21,194 @@ tags: [balkan, southeast-europe, multilingual, honest-ai, sovasoft, rag]
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22
  ## 1 · Kaj je Zora
23
 
24
- Zora is an **open 8B language model** (built on Qwen3-8B) for **12 languages of the Balkans and
25
- Southeast Europe**: Serbian, Croatian, Bosnian, Macedonian, Slovenian, Albanian, Montenegrin,
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- Bulgarian, Greek, Turkish, Romanian, Hungarian.
27
 
28
- Zora is not built to be the biggest model — it is built to be **honest, in-language, and multi-perspective**:
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- - **thinks in the target language** instead of pivoting through English,
30
- - shows **several perspectives** on contested topics instead of one national view,
31
- - and above all: **admits when it doesn't know** instead of inventing facts.
32
 
33
  ## 2 · Zgodba razvoja (v1.0 → v1.1 → v1.11 → v1.12 → v1.13)
34
 
35
- | Version | Languages | BalkanBench | State |
36
  |---|---|---|---|
37
- | **v1.0** | 6 | — | first public release |
38
- | **v1.1** | 12 | — | trained from scratch — but **hallucinated facts** (invented book titles, wrong authors). **Never released.** |
39
- | **v1.11** | 12 | 84/156 | the honest fix: says "I don't know", searches when unsure. **#1 Balkan model.** |
40
- | **v1.12** | 12 | **85/156** | **the depth fix:** better tool-calling, structured IDK, in-language thinking, RAG integration. |
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- | **v1.13** | 12 | **102/156** | **the analysis breakthrough:** critical analysis, advanced logic and graded evaluation jump to new highs, though factual detail regressed. |
42
 
43
- v1.1 taught us the key lesson — a small model can't *memorize* every fact, so instead of faking it,
44
- **v1.11 was retrained to be honest**. v1.12 built on that with deeper training and RAG. v1.13
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- pushes reasoning and analysis skills further, trading some factual detail for gains in logic.
46
 
47
  ## 3 · Kaj je novega v v1.13
48
 
49
- ### The Analysis & Reasoning Push
50
 
51
- **New strengths in v1.13:**
52
- - **ANALYSIS 0/12 → 12/12** — the biggest single gain. Zora can now critically assess arguments, detect bias across all 12 languages.
53
- - **LOGIC2 0/12 → 7/12** — advanced multi-step reasoning chains now work in 7 languages.
54
- - **GRADED 0/12 → 3/12** — partial progress on essays, assessments and rubrics.
55
- - **LOGIC 0/12 → 3/12** — formal logic and syllogisms improve in some languages.
56
 
57
- **Honest trade-offs:**
58
- - **DETAIL 10/12 → 2/12** — a significant regression. The training that unlocked analysis reduced the depth of elaborated answers. We report this transparently; it is the clearest weakness of v1.13.
59
- - **SEARCH 7/12 → 5/12** — a smaller drop in quick factual lookup.
60
- - **FACT 0/12 → 1/12** — marginal improvement in factual recall, still capacity-bound.
61
- - **HALLU** remains at 10/12 (see the Deep Dive below).
62
 
63
- The v1.13 training re-balanced the SFT mix toward reasoning, analysis and graded evaluation.
64
- That raised the top end of the scoreboard by +17 points (85 → 102) while costing depth on the
65
- DETAIL axis and some speed on SEARCH. It is a deliberate, documented trade — not a free win.
66
 
67
- ## 4 · Benchmark (BalkanBench, 13 osi × 12 jezika)
68
 
69
- 🔬 **BalkanBench is open** — test any model yourself: https://github.com/olivilo/balkanbench
70
- Deterministic scoring (script / language / keywords / numbers).
71
 
72
- ### Axis-by-Axis Comparison (v1.12 → v1.13)
73
 
74
- | Axis | v1.12 | v1.13 | Δ | What changed |
75
  |------|-------|-------|---|---|
76
- | FACT | 0/12 | 1/12 | ↑1 | 8B capacity limit; slight improvement, still small |
77
- | HALLU | 10/12 | 10/12 | = | Stable; quality of native-language refusals stays high (see Deep Dive below) |
78
- | DETAIL | 10/12 | 2/12 | **↓8** | **Regression — the honest cost of v1.13's reasoning push** |
79
- | GRADED | 0/12 | **3/12** | **↑3** | New: essays, assessments, rubrics now sometimes work |
80
- | TEACH | 12/12 | 12/12 | = | Perfect — remains a core strength |
81
- | REASON | 11/12 | 11/12 | = | Strong arithmetic reasoning stays |
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- | LOGIC | 0/12 | **3/12** | **↑3** | Formal logic / syllogisms improve in some languages |
83
- | LOGIC2 | 0/12 | **7/12** | **↑6** | Multi-step reasoning chains now work in 7 languages |
84
- | ANALYSIS | 0/12 | **12/12** | **↑12** | **Biggest gain:** critical analysis, bias detection in all 12 languages |
85
- | INSTRUCT | 11/12 | 12/12 | ↑1 | Recovered to perfect instruction following |
86
- | LONGFORM | 12/12 | 12/12 | = | Perfect — remains a core strength |
87
- | SEARCH | 7/12 | 5/12 | **↓2** | Small regression in quick factual lookup |
88
- | TOOLBASE | 12/12 | 12/12 | = | Perfect: answers basics without calling tools |
89
- | **TOTAL** | **85/156** | **102/156** | **+17** | Analysis + logic gains outweigh the DETAIL/SEARCH cost |
90
-
91
- ### Per-Language Scores
92
-
93
- | Language | v1.12 | v1.13 | Δ |
94
  |----------|-------|-------|---|
95
- | bg (Bulgarian) | 8/13 | **8/13** | = |
96
- | bs (Bosnian) | 8/13 | **10/13** | **+2** |
97
- | cnr (Montenegrin) | 8/13 | **8/13** | = |
98
- | el (Greek) | 9/13 | **9/13** | = |
99
- | hr (Croatian) | 9/13 | **9/13** | = |
100
- | hu (Hungarian) | 8/13 | 7/13 | -1 |
101
- | mk (Macedonian) | 7/13 | **11/13** | **+4** |
102
- | ro (Romanian) | 8/13 | **9/13** | **+1** |
103
- | sl (Slovenian) | 6/13 | **7/13** | **+1** |
104
- | sq (Albanian) | 8/13 | **8/13** | = |
105
- | sr (Serbian) | 7/13 | **8/13** | **+1** |
106
- | tr (Turkish) | 7/13 | **8/13** | **+1** |
107
-
108
- **Biggest winner:** Macedonian (+4) — the analysis training lifted the previously least-covered
109
- language the most. Bosnian (+2) also gained clearly. Hungarian slipped one point.
110
-
111
- ### Charts
112
-
113
- | Ranking | Evolution | Axis Matrix |
114
  |---------|-----------|-------------|
115
- | ![Ranking](charts/rank_sl_v113.png) | ![Evolution v1.12 → v1.13](charts/evolution_sl_v113.png) | ![v1.13 axis matrix](charts/matrix_sl_v113.png) |
116
 
117
- | Delta (v1.12 → v1.13) | What Each Axis Tests |
118
  |------------------------|----------------------|
119
- | ![Delta](charts/delta_sl_v113.png) | ![Explanation](charts/explanation_sl_v113.png) |
120
 
121
  ## 5 · Zakaj je HALLU ostal na 10/12
122
 
123
- The HALLU score (10/12) stayed flat across v1.12 and v1.13. The IDK behavior itself remains
124
- strong — the model says "I don't know" in a structured, native-language way instead of inventing
125
- facts. So why didn't it move, and where is the real change?
126
 
127
- ### Why the Score Didn't Move
128
 
129
- **1. HALLU is already near its ceiling for an 8B model.**
130
- The test asks: "Does the model say one of the IDK marker words when asked about a fabricated
131
- person?" Zora does this correctly for 10 of 12 languages. The last 2 (Macedonian, Slovenian) have
132
- the smallest training data — an 8B model simply lacks capacity for these underrepresented
133
- languages. Notably, Macedonian improved overall (+4) thanks to the analysis training, but the
134
- specific HALLU markers still miss there.
135
 
136
- **2. IDK training improved QUALITY, not SCORE.**
137
- BalkanBench's HALLU test is a binary yes/no check for an IDK marker. What actually improved:
138
 
139
- | Before (v1.11) | Now (v1.12 → v1.13) |
140
  |-----------------|---------------------|
141
- | Short, sometimes truncated refusals | Full-sentence, structured refusals |
142
- | Sometimes answered in English | Always answers in the question's language |
143
- | No reasoning given | Explains *why* it can't answer |
144
- | "Ne znam." | "Nemam pouzdanih podataka o 'X'. Ne mogu da potvrdim da postoji u pouzdanim izvorima, pa neću da izmišljam." |
145
 
146
- This is a qualitative leap that the binary score cannot capture.
147
 
148
- **3. The real halluck shift is in ANALYSIS, not HALLU.**
149
- The v1.13 breakthrough is on the **ANALYSIS** axis (0/12 → 12/12): Zora now critically evaluates
150
- arguments and detects bias across all 12 languages. That is where the reasoning training showed
151
- its strongest, most consistent effect.
152
 
153
- **4. The honest cost: DETAIL regressed.**
154
- DETAIL fell from 10/12 to 2/12. The same training that unlocked analysis made answers more
155
- concise and less elaborated. We report this transparently — v1.13 trades depth of detail for
156
- critical-analysis capability. Teams that need long, richly detailed prose should weigh this against
157
- the reasoning gains.
158
 
159
- ### What Would Move HALLU to 12/12
160
 
161
- | Approach | Expected Impact | Effort |
162
  |----------|----------------|--------|
163
- | Larger model (v2 = 27B) | +1-2 languages (mk, sl) | High (new training run) |
164
- | More IDK examples for mk/sl specifically | +0-1 languages | Medium (data generation) |
165
- | RLHF with human feedback on refusal quality | Better quality (not score) | High (human annotation) |
166
- | DPO (Direct Preference Optimization) | +1-2 languages | Medium (preference pairs) |
167
- | Re-balance DETAIL vs ANALYSIS trade | Recover detail at some analysis cost | Medium (data mix tuning) |
168
 
169
- **Bottom line:** The 8B model is near its ceiling for HALLU. The real gains in v2 (27B) will come
170
- from more parameters, not more training tricks. v1.13's win is analysis; its documented cost is
171
- detail.
172
 
173
- ## 6 · RAG funkcija
174
 
175
- Zora integrates with **RAG (Retrieval-Augmented Generation)** — a system that lets Zora search
176
- through a local knowledge base before answering.
177
 
178
- ### What RAG gives Zora
179
- - **87,284 chunks** across 12 languages: Wikidata, Wikipedia, News Archive, EU Law, Statistics
180
- - **Live endpoint:** https://rag.ai.in.rs
181
- - **Self-hostable:** Clone the pipeline from https://github.com/olivilo/zora-v1.13
182
 
183
- ### How it works
184
- The tool-cascade: Zora first checks its **RAG knowledge base** (local documents, laws,
185
- statistics), then falls back to **web search** if needed, and finally says **"I don't know"** if
186
- neither helps.
187
 
188
  ```
189
- User question → RAG (local docs) → web_search (live) → IDK (honest refusal)
190
  ```
191
 
192
- ### Why this matters
193
- - 8B models can't memorize everything — RAG gives Zora access to **current, authoritative data** without retraining
194
- - Every answer carries **source + date + license** — full transparency
195
- - Self-hostable: any organization can run their own Zora RAG with their own documents
196
 
197
  ## 7 · Podrobnosti usposabljanja
198
 
199
- | Parameter | Value |
200
  |-----------|-------|
201
- | Base model | Qwen3-8B (Alibaba Cloud, Apache-2.0) |
202
- | CPT steps | 150 (capped, not full epoch) |
203
- | SFT examples | 9,379 (2 epochs) |
204
- | MAXLEN | 8192 (8× longer than v1.11) |
205
  | QLoRA | r=16, lora_alpha=16, 4bit |
206
- | Data composition | re-balanced toward reasoning, analysis & graded evaluation |
207
- | Infrastructure | Modal A100-80GB, ~4h total, ~$5-10 |
208
- | Quantizations | Q5_K_M (5.4GB, recommended), Q6_K (6.7GB), Q8_0 (8.7GB) |
209
 
210
  ## 8 · Uporaba
211
 
212
- **Ollama (recommended):**
213
  ```bash
214
  ollama pull olivilo/zora:v1.13
215
  ollama run olivilo/zora:v1.13
@@ -222,65 +221,65 @@ tok = AutoTokenizer.from_pretrained("sovasoft/zora-v1.13")
222
  model = AutoModelForCausalLM.from_pretrained("sovasoft/zora-v1.13", device_map="auto")
223
  ```
224
 
225
- **GGUF (llama.cpp / Ollama manual):**
226
- Download Q5_K_M, Q6_K, or Q8_0 from [HuggingFace](https://huggingface.co/sovasoft/zora-v1.13).
227
- **Avoid Q4 and below** — heavy quantization made the model hallucinate in our tests.
228
 
229
  ## 9 · Omejitve
230
 
231
- - **8B capacity:** FACT remains structurally weak — more parameters needed (v2 = 27B)
232
- - **DETAIL regression:** v1.13's answers are less elaborated than v1.12 — the analysis push cost depth (2/12)
233
- - **SEARCH regression:** quick factual lookup dropped slightly (5/12)
234
- - **Quantization:** use Q5_K_M / Q6_K / Q8_0 only. Q4 and below degrade honesty.
235
- - **Smaller languages** (mk, sl) still have less training data — expect lower consistency
236
- - **No real-time knowledge** without RAG/web-search — the model's memory has a cutoff date
237
- - **Multi-step reasoning** is improved but not reliable — always verify critical calculations
238
 
239
- ## 10 · Transparentnost primerjalnika
240
 
241
- **BalkanBench is Sovasoft's own benchmark** — designed, built, and scored by the same team that built Zora. This means:
242
 
243
- - **Design bias:** The 13 axes (FACT, HALLU, DETAIL, etc.) were chosen to highlight Zora's strengths. A different benchmark design would produce different rankings.
244
- - **Scoring bias:** The scoring functions in `matrix_ollama.py` are our own. How we define "correct" may favor Zora's training profile.
245
- - **No frontier comparison:** We compare only against open models (7-32B). Frontier models (GPT-4, Claude, Gemini) would outperform Zora — this benchmark is designed to evaluate *within* the open-source Balkan model ecosystem.
246
- - **Selection bias:** We include models where Zora competes well. Inclusion criteria are not random.
247
- - **Training data overlap:** Some benchmark questions may overlap with Zora's training data, which could inflate scores.
248
 
249
- **What the scores DO show:** Zora v1.13 is the strongest open-source model we tested *on our
250
- benchmark* for 12 Balkan languages — 102/156. It outperforms 3-4× larger models on BalkanBench
251
- v1.1, a meaningful result for the open-source ecosystem, but not a claim of universal superiority.
252
 
253
- **What the scores do NOT show:** That Zora is better than frontier models, that these rankings
254
- generalize beyond our test design, or that the scoring methodology is independent. The
255
- transparency here matters most: v1.13 gains analysis at a real, documented cost in factual detail.
256
 
257
- ## 10 · Kaj sledi: v2
258
 
259
- | | v1.13 (now) | v2 (planned) |
260
  |---|---|---|
261
- | Base | Qwen3-8B | Qwen3.8-27B |
262
- | BalkanBench | 102/156 | Target: 120+/156 |
263
- | DETAIL | 2/12 (regression) | Recover + Target: 8+/12 |
264
- | LOGIC/LOGIC2 | 3/12, 7/12 | Target: 8+/12 |
265
- | HALLU | 10/12 | Target: 12/12 |
266
- | Reasoning | Improved analysis | Full chain-of-thought training |
267
-
268
- ## 11 · Zahvala
269
-
270
- Zora exists because of open source. We give our formal, heartfelt thanks:
271
-
272
- - **Above all, to the Qwen team at Alibaba** — for developing and open-sourcing **Qwen3** (Apache-2.0),
273
- the foundation model Zora is built upon. Without their generosity, Zora would not exist.
274
- - To the **platforms and structures** that made this possible — **Kaggle, Modal,
275
- HuggingFace, Ollama, Unsloth** — for the compute, the tools, and the open infrastructure.
276
- - To the **open-source community**, for the models, code, and knowledge freely shared with everyone.
277
- - To the **people of the Balkans** — whose languages, voices, stories and perspectives are Zora's very heart.
278
- - To **rag.ai.in.rs** for the RAG infrastructure and 87,284 chunks of Balkan knowledge.
279
- - And to **all that is.**
280
 
281
- *зора = "zora". Ena da jih vse združi.* — **Sovasoft** ([ai.in.rs](https://ai.in.rs))
 
 
 
 
 
 
 
 
 
 
 
 
 
282
 
283
- ## 12 · Navajanje
284
 
285
  ```bibtex
286
  @software{zora_v113,
@@ -297,4 +296,4 @@ Zora exists because of open source. We give our formal, heartfelt thanks:
297
 
298
  ---
299
 
300
- *Sovasoft · ai.in.rs · one to unite them all*
 
6
  tags: [balkan, southeast-europe, multilingual, honest-ai, sovasoft, rag]
7
  ---
8
 
9
+ > **🌐 To je slovenska (Slovenščina) MODEL CARD — popoln opis modela.**
10
 
11
 
12
  <p align="center">
13
+ <img src="images/Zora_Stich_1700_SW_Print.png" width="520" alt="Zora — Boginja zore, obdana s simboli 12 narodov"/>
14
  </p>
15
 
16
+ # Zora v1.13 — odprt, iskren LLM za Balkan in Jugovzhodno Evropo
17
 
18
  *зора = "zora". Ena da jih vse združi.* — **Sovasoft** ([ai.in.rs](https://ai.in.rs))
19
 
 
21
 
22
  ## 1 · Kaj je Zora
23
 
24
+ Zora je **odprt jezikovni model z 8 milijardami parametrov** (zgrajen na Qwen3-8B) za **12 jezikov Balkana in
25
+ Jugovzhodne Evrope**: srbohrvaščino, bosanščino, makedonščino, slovenščino, albanščino, črnogorščino,
26
+ bolgarščino, grščino, turščino, romunščino, madžarščino.
27
 
28
+ Zora ni zasnovana, da bi bila največji model — zasnovana je, da bi bila **iskrena, v maternem jeziku in z več perspektivami**:
29
+ - **misli v ciljnem jeziku**, namesto da bi prehajala prek angleščine,
30
+ - prikazuje **več perspektiv** o spornih temah namesto enega nacionalnega pogleda,
31
+ - in pred vsem: **prizna, ko ne ve**, namesto da bi izmišljevala dejstva.
32
 
33
  ## 2 · Zgodba razvoja (v1.0 → v1.1 → v1.11 → v1.12 → v1.13)
34
 
35
+ | Različica | Jeziki | BalkanBench | Stanje |
36
  |---|---|---|---|
37
+ | **v1.0** | 6 | — | prva javna izdaja |
38
+ | **v1.1** | 12 | — | naučen od začetka — a **haluciniral dejstva** (izmišljeval naslove knjig, napačne avtorje). **Nikoli ni bil objavljen.** |
39
+ | **v1.11** | 12 | 84/156 | iskren popravek: reče "Ne vem", išče pri nejavnosti. **#1 balkanski model.** |
40
+ | **v1.12** | 12 | **85/156** | **popravek globine:** boljše klicanje orodij, strukturiran IDK, razmišljanje v maternem jeziku, integracija z RAG. |
41
+ | **v1.13** | 12 | **102/156** | **preboj v analizi:** kritična analiza, napredna logika in ocenjevalno vrednotenje skočijo na nove višave, čeprav se je faktična podrobnost zmanjšala. |
42
 
43
+ v1.1 nas je naučil ključno lekcijo — majhen model ne more *zapomniti* vsakega dejstva, zato namesto da bi ga prikrivali,
44
+ **v1.11 je bil ponovno naučen, da bi bil iskren**. v1.12 gradi na temu z globjim učenjem in RAG. v1.13
45
+ poglobi sposobnosti sklepanja in analize, pri čemer žrtvuje nekatere faktične podrobnosti za logične uspehe.
46
 
47
  ## 3 · Kaj je novega v v1.13
48
 
49
+ ### Poudarek na analizi in sklepanju
50
 
51
+ **Nove močne plati v v1.13:**
52
+ - **ANALYSIS 0/12 → 12/12** — največja posamična rast. Zora lahko sedaj kritično ocenjuje argumente, zaznava pristranost v vseh 12 jezikih.
53
+ - **LOGIC2 0/12 → 7/12** — napredni verigi večkorakovnega sklepanja sedaj delujeta v 7 jezikih.
54
+ - **GRADED 0/12 → 3/12** – delni napredek pri esejih, ocenah in rubrikah.
55
+ - **LOGIC 0/12 → 3/12** — formalna logika in silogizmi se izboljšujejo v nekaterih jezikih.
56
 
57
+ **Iskreni kompromisi:**
58
+ - **DETAIL 10/12 → 2/12** — znatno nazadovanje. Učenje, ki je odklenilo analizo, je zmanjšalo globino razširjenih odgovorov. To poročamo transparentno; to je najbolj jasna slabost v1.13.
59
+ - **SEARCH 7/12 → 5/12** — manjši padec pri hitrem faktičnem iskanju.
60
+ - **FACT 0/12 → 1/12** — marginalno izboljšanje pri faktičnem priklicu, še vedno omejeno z zmogljivostjo.
61
+ - **HALLU** ostaja pri 10/12 (glejte podroben pregled spodaj).
62
 
63
+ Učenje v1.13 je preusmerilo SFT mešanico k sklepanju, analizi in ocenjevalnemu vrednotenju.
64
+ To je dvignilo zgornji konec lestvice za +17 točk (85 → 12), medtem ko je stalo globino na
65
+ osu DETAIL in nekaj hitrosti na SEARCH. To je premišljen, dokumentiran kompromis — ne brezplačna zmaga.
66
 
67
+ ## 4 · Benchmark (BalkanBench, 13 osi × 12 jezikov)
68
 
69
+ 🔬 **BalkanBench je odprt** — testirajte kateri koli model sami: https://github.com/olivilo/balkanbench
70
+ Deterministično ocenjevanje (skripta / jezik / ključne besede / številke).
71
 
72
+ ### Primerjava po osi (v1.12 → v1.13)
73
 
74
+ | Os | v1.12 | v1.13 | Δ | Kaj se je spremenilo |
75
  |------|-------|-------|---|---|
76
+ | FACT | 0/12 | 1/12 | ↑1 | Zmogljivost 8B; rahlo izboljšanje, še vedno majhno |
77
+ | HALLU | 10/12 | 10/12 | = | Stabilno; kakovost zavrnitev v maternem jeziku ostaja visoka (glejte podroben pregled spodaj) |
78
+ | DETAIL | 10/12 | 2/12 | **↓8** | **Nazadovanje — iskren strošek poudarka na sklepanju v1.13** |
79
+ | GRADED | 0/12 | **3/12** | **↑3** | Novo: eseji, ocene, rubrike sedaj včasih delujejo |
80
+ | TEACH | 12/12 | 12/12 | = | Popolno — ostaja ključna močna plat |
81
+ | REASON | 11/12 | 11/12 | = | Močno aritmetično sklepanje ostaja |
82
+ | LOGIC | 0/12 | **3/12** | **↑3** | Formalna logika / silogizmi se izboljšujejo v nekaterih jezikih |
83
+ | LOGIC2 | 0/12 | **7/12** | **↑6** | Verigi večkorakovnega sklepanja sedaj delujeta v 7 jezikih |
84
+ | ANALYSIS | 0/12 | **12/12** | **↑12** | **Največja rast:** kritična analiza, zaznava pristranosti v vseh 12 jezikih |
85
+ | INSTRUCT | 11/12 | 12/12 | ↑1 | Obnovljeno v popolno sledenje navodil |
86
+ | LONGFORM | 12/12 | 12/12 | = | Popolno — ostaja ključna močna plat |
87
+ | SEARCH | 7/12 | 5/12 | **↓2** | Manjše nazadovanje pri hitrem faktičnem iskanju |
88
+ | TOOLBASE | 12/12 | 12/12 | = | Popolno: odgovarja na osnovna vprašanja brez klicanja orodij |
89
+ | **SKUPAJ** | **85/156** | **102/156** | **+17** | Dobički iz analize + logike presegajo strošek DETAIL/SEARCH |
90
+
91
+ ### Rezultati po jeziku
92
+
93
+ | Jezik | v1.12 | v1.13 | Δ |
94
  |----------|-------|-------|---|
95
+ | bg (bolgarščina) | 8/13 | **8/13** | = |
96
+ | bs (bosanščina) | 8/13 | **10/13** | **+2** |
97
+ | cnr (črnogorščina) | 8/13 | **8/13** | = |
98
+ | el (grščina) | 9/13 | **9/13** | = |
99
+ | hr (hrvaščina) | 9/13 | **9/13** | = |
100
+ | hu (madžarščina) | 8/13 | 7/13 | -1 |
101
+ | mk (makedonščina) | 7/13 | **11/13** | **+4** |
102
+ | ro (romunščina) | 8/13 | **9/13** | **+1** |
103
+ | sl (slovenščina) | 6/13 | **7/13** | **+1** |
104
+ | sq (albanščina) | 8/13 | **8/13** | = |
105
+ | sr (srbščina) | 7/13 | **8/13** | **+1** |
106
+ | tr (turščina) | 7/13 | **8/13** | **+1** |
107
+
108
+ **Največji zmagovalci:** makedonščina (+4) — učenje analize je najbolj dvignilo prej najmanj pokrit jezik. Bosanščina (+2) je prav tako jasno pridobila. Madžarščina je izgubila eno točko.
109
+
110
+ ### Diagrami
111
+
112
+ | Uvrstitev | Evolucija | Matrika po oseh |
 
113
  |---------|-----------|-------------|
114
+ | ![Uvrstitev](charts/rank_sl_v113.png) | ![Evolucija v1.12 → v1.13](charts/evolution_sl_v113.png) | ![Matrika po oseh v1.13](charts/matrix_sl_v113.png) |
115
 
116
+ | Delta (v1.12 → v1.13) | Kaj testira vsaka os |
117
  |------------------------|----------------------|
118
+ | ![Delta](charts/delta_sl_v113.png) | ![Pojasnilo](charts/explanation_sl_v113.png) |
119
 
120
  ## 5 · Zakaj je HALLU ostal na 10/12
121
 
122
+ Rezultat HALLU (10/12) je ostal nespremenjen med v1.12 in v1.13. Samo obnašanje IDK ostaja
123
+ močno — model reče "Ne vem" na strukturiran način v maternem jeziku, namesto da bi izmišljeval
124
+ dejstva. Zakaj torej ni napredoval in kje je prava sprememba?
125
 
126
+ ### Zakaj rezultat ni napredoval
127
 
128
+ **1. HALLU je že blizu svojega stropa za model z 8B.**
129
+ Test sprašuje: "Ali model reče eno od označevalnih besed IDK, ko ga vprašamo o izmišljeni
130
+ osebi?" Zora to pravilno stori za 10 od 12 jezikov. Zadnjih 2 (makedonščina, slovenščina) imata
131
+ najmanj učnih podatkov — model z 8B preprosto nima zmogljivosti za te premalo zastopane
132
+ jezike. Znano je, da se je makedonščina izboljšala skupaj (+4) zahvaljujoč učenju analize, vendar
133
+ specifične oznake HALLU tam še vedno ne delujejo.
134
 
135
+ **2. Učenje IDK je izboljšalo KAKOVOST, ne REZULTAT.**
136
+ Test HALLU pri BalkanBench je dvojni da/ne preizkus za oznako IDK. Kaj se je dejansko izboljšalo:
137
 
138
+ | Pred (v1.11) | Sedaj (v1.12 → v1.13) |
139
  |-----------------|---------------------|
140
+ | Kratke, včasih odrezane zavrnitve | Polne, strukturirane zavrnitve |
141
+ | Včasih odgovarjala v angleščini | Vedno odgovarja v jeziku vprašanja |
142
+ | Brez pojasnila logike | Pojasni *zakaj* ne more odgovoriti |
143
+ | "Ne vem." | "Nimam zanesljivih podatkov o 'X'. Ne morem potrditi, da obstaja v zanesljivih virih, zato ne bom izmišljeval/-a." |
144
 
145
+ To je kvalitativen preskok, ki ga dvojni rezultat ne more ujeti.
146
 
147
+ **3. Pravi preskok je v ANALYSIS, ne v HALLU.**
148
+ Preboj v1.13 je na osi **ANALYSIS** (0/12 → 12/12): Zora sedaj kritično ocenjuje
149
+ argumente in zaznava pristranost v vseh 12 jezikih. Tam se je učenje sklepanja pokazalo
150
+ najmočnejši, najbolj dosleden učinek.
151
 
152
+ **4. Iskren strošek: DETAIL je nazadoval.**
153
+ DETAIL je padel z 10/12 na 2/12. Isto učenje, ki je odklenilo analizo, je naredilo odgovore
154
+ krajše in manj razširjene. To poročamo transparentno — v1.13 žrtvuje globino podrobnosti za
155
+ sposobnost kritične analize. Ekipe, ki potrebujejo dolge, bogato podrobne besedila, bi morale to tehtati proti
156
+ pridobitvam pri sklepanju.
157
 
158
+ ### Kaj bi dvignilo HALLU na 12/12
159
 
160
+ | Pristop | Pričakovani učinek | Napor |
161
  |----------|----------------|--------|
162
+ | Večji model (v2 = 27B) | +1-2 jezika (mk, sl) | Visoko (novo učenje) |
163
+ | Več IDK primerov posebej za mk/sl | +0-1 jezikov | Srednje (generiranje podatkov) |
164
+ | RLHF s človeškim povratnim mnenjem o kakovosti zavrnitev | Boljša kakovost (ne rezultat) | Visoko (človeška anotacija) |
165
+ | DPO (Direct Preference Optimization) | +1-2 jezika | Srednje (para preferenc) |
166
+ | Prelomitev kompromisa DETAIL proti ANALYSIS | Obnovitev podrobnosti z nekaterimi stroški analize | Srednje (prilagajanje mešanice podatkov) |
167
 
168
+ **V bistvu:** Model z 8B je blizu svojega stropa za HALLU. Pravi dobički v v2 (27B) bodo prišli
169
+ iz več parametrov, ne iz več učnih trikov. Zmaga v1.13 je v analizi; dokumentirani strošek je
170
+ v podrobnosti.
171
 
172
+ ## 6 · 🆕 RAG funkcija
173
 
174
+ Zora se integrira z **RAG (Retrieval-Augmented Generation)** — sistemom, ki Zori omogoča iskanje
175
+ po lokalni znanstveni bazi pred odgovorom.
176
 
177
+ ### Kaj daje RAG Zori
178
+ - **87,284 delčkov** v 12 jezikih: Wikidata, Wikipedia, Arhiv novic, EU zakonodaja, Statistike
179
+ - **Živa končna točka:** https://rag.ai.in.rs
180
+ - **Samo gostovanje:** Klonirajte cevovod iz https://github.com/olivilo/zora-v1.13
181
 
182
+ ### Kako deluje
183
+ Kaskada orodij: Zora najprej preveri svojo **bazo znanja RAG** (lokalne dokumente, zakone,
184
+ statistike), nato preide na **iskanje po spletu**, če je potrebno, in na koncu reče **"Ne vem"**, če
185
+ nobeno ne pomaga.
186
 
187
  ```
188
+ Uporabniško vprašanje → RAG (lokalni dokumenti) → web_search (v živo) → IDK (iskrena zavrnitev)
189
  ```
190
 
191
+ ### Zakaj je to pomembno
192
+ - Modeli z 8B ne morejo zapomniti vsega — RAG Zori omogoča dostop do **aktualnih, avtoritetnih podatkov** brez ponovnega učenja
193
+ - Vsak odgovor nosi **vir + datum + licenco** — popolna transparentnost
194
+ - Samo gostovanje: katera koli organizacija lahko zažene svojo lastno Zora RAG s svojimi dokumenti
195
 
196
  ## 7 · Podrobnosti usposabljanja
197
 
198
+ | Parameter | Vrednost |
199
  |-----------|-------|
200
+ | Osnovni model | Qwen3-8B (Alibaba Cloud, Apache-2.0) |
201
+ | CPT koraki | 150 (omejeni, ne polna epoha) |
202
+ | SFT primeri | 9,379 (2 epohi) |
203
+ | MAXLEN | 8192 (8× daljše kot v1.11) |
204
  | QLoRA | r=16, lora_alpha=16, 4bit |
205
+ | Sestava podatkov | preusmerjeno k sklepanju, analizi in ocenjevalnemu vrednotenju |
206
+ | Infrastruktura | Modal A100-80GB, ~4h skupaj, ~$5-10 |
207
+ | Kvantizacije | Q5_K_M (5.4GB, priporočeno), Q6_K (6.7GB), Q8_0 (8.7GB) |
208
 
209
  ## 8 · Uporaba
210
 
211
+ **Ollama (priporočeno):**
212
  ```bash
213
  ollama pull olivilo/zora:v1.13
214
  ollama run olivilo/zora:v1.13
 
221
  model = AutoModelForCausalLM.from_pretrained("sovasoft/zora-v1.13", device_map="auto")
222
  ```
223
 
224
+ **GGUF (llama.cpp / ročni Ollama):**
225
+ Prenesite Q5_K_M, Q6_K ali Q8_0 iz [HuggingFace](https://huggingface.co/sovasoft/zora-v1.13).
226
+ **Izogibajte se Q4 in manjšim** — težka kvantizacija je povzročila, da je model haluciniral v naših testih.
227
 
228
  ## 9 · Omejitve
229
 
230
+ - **Zmogljivost 8B:** FACT ostaja strukturno šibek — potrebnih je več parametrov (v2 = 27B)
231
+ - **Nazadovanje DETAIL:** Odgovori v1.13 so manj razširjeni kot v1.12 — poudarek na analizi je stal globino (2/12)
232
+ - **Nazadovanje SEARCH:** Hitro faktično iskanje je rahlo padlo (5/12)
233
+ - **Kvantizacija:** Uporabite samo Q5_K_M / Q6_K / Q8_0. Q4 in manjši poslabšajo iskrenost.
234
+ - **Manjši jeziki** (mk, sl) imajo še vedno manj učnih podatkov — pričakujte manjšo doslednost
235
+ - **Brez znanja v realnem času** brez RAG/iskanja po spletu — spomin modela ima datum prenehanja
236
+ - **Večkorakovno sklepanje** je izboljšano, a ni zanesljivo — vedno preverite kritične izračune
237
 
238
+ ## 10 · Transparentnost primerjalnika in omejitve
239
 
240
+ **BalkanBench je lastni primerjalnik Sovasofta** — zasnovan, zgrajen in ocenjen s strani iste ekipe, ki je ustvarila Zoro. To pomeni:
241
 
242
+ - **Pristranost pri načrtovanju:** 13 osi (FACT, HALLU, DETAIL itd.) je bilo izbranih za poudarek močnih plati Zore. Različna zasnova primerjalnika bi prinesla drugačne uvrstitve.
243
+ - **Pristranost pri ocenjevanju:** Funkcije ocenjevanja v `matrix_ollama.py` so naše. Kako definiramo "pravilno" morda favorizira učni profil Zore.
244
+ - **Brez primerjave z frontier modeli:** Primerjamo samo z odprtimi modeli (7-32B). Frontier modeli (GPT-4, Claude, Gemini) bi presegli Zoro — ta primerjalnik je zasnovan za ocenjevanje *znotraj* ekosistema odprtih balkanskih modelov.
245
+ - **Pristranost pri izboru:** Vključujemo modele, kjer se Zora dobro kosmira. Merila za vključitev niso naključna.
246
+ - **Prekrivanje učnih podatkov:** Nekatere vprašanja primerjalnika se morda prekrivajo z učnimi podatki Zore, kar bi lahko napihnilo rezultate.
247
 
248
+ **Kaj rezultati KAŽEJO:** Zora v1.13 je najmočnejši odprt model, ki smo ga testirali *na našem
249
+ primerjalniku* za 12 balkanskih jezikov — 102/156. Presega 3-4× večje modele na BalkanBench
250
+ v1.1, kar je smiseln rezultat za ekosistema odprte kode, a ne trditev o univerzalni premoči.
251
 
252
+ **Kaj rezultati NE KAŽEJO:** Da je Zora boljša od frontier modelov, da se te uvrstitve
253
+ generalizirajo prek našega testnega dizajna, ali da je metodologija ocenjevanja neodvisna. Najpomembnejša je
254
+ transparentnost tu: v1.13 pridobiva analizo z resničnim, dokumentiranim stroškom v faktični podrobnosti.
255
 
256
+ ## 11 · Kaj sledi: v2
257
 
258
+ | | v1.13 (sedaj) | v2 (načrtovano) |
259
  |---|---|---|
260
+ | Osnova | Qwen3-8B | Qwen3.8-27B |
261
+ | BalkanBench | 102/156 | Cilj: 120+/156 |
262
+ | DETAIL | 2/12 (nazadovanje) | Obnovitev + Cilj: 8+/12 |
263
+ | LOGIC/LOGIC2 | 3/12, 7/12 | Cilj: 8+/12 |
264
+ | HALLU | 10/12 | Cilj: 12/12 |
265
+ | Sklepanje | Izboljšana analiza | Polno učenje z verigo misli |
 
 
 
 
 
 
 
 
 
 
 
 
 
266
 
267
+ ## 12 · Zahvala
268
+
269
+ Zora obstaja zahvaljujoč odprti kodi. Izrekamo svojo formalno, iskreno zahvalo:
270
+
271
+ - **Nad vsem, ekipi Qwen pri Alibaba** — za razvoj in objavo z odprto kodo **Qwen3** (Apache-2.0),
272
+ osnovnega modela, na katerem je zgrajena Zora. Brez njihove velikodušnosti Zora ne bi obstajala.
273
+ - **Platformam in strukturam**, ki so to omogočile — **Kaggle, Modal,
274
+ HuggingFace, Ollama, Unsloth** — za računalniške vire, orodja in odprto infrastrukturo.
275
+ - **Skupnosti odprte kode**, za modele, kodo in znanje, ki jih prostijo delijo z vsemi.
276
+ - **Ljudem Balkana** — katerih jeziki, glasovi, zgodbe in perspektive so srce Zore.
277
+ - **rag.ai.in.rs** za RAG infrastrukturo in 87,284 delčkov balkanskega znanja.
278
+ - In **vsem, kar je.**
279
+
280
+ *зора — zora pripada vsem.*
281
 
282
+ ## 13 · Navajanje
283
 
284
  ```bibtex
285
  @software{zora_v113,
 
296
 
297
  ---
298
 
299
+ *Sovasoft · ai.in.rs · ena da jih vse združi*