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@@ -6,11 +6,8 @@ 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 Serbian (Srpski) 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"/>
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  </p>
15
 
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  # Zora v1.13 — otvoreni, iskreni LLM za Balkan i Jugoistočnu Evropu
@@ -21,195 +18,192 @@ tags: [balkan, southeast-europe, multilingual, honest-ai, sovasoft, rag]
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22
  ## 1 · Šta 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,
26
- 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**:
29
- - **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 · Priča o razvoju (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. |
41
- | **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
45
- pushes reasoning and analysis skills further, trading some factual detail for gains in logic.
46
 
47
- ## 3 · Šta je novo 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 |
82
- | 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_sr_v113.png) | ![Evolution v1.12 → v1.13](charts/evolution_sr_v113.png) | ![v1.13 axis matrix](charts/matrix_sr_v113.png) |
116
 
117
- | Delta (v1.12 → v1.13) | What Each Axis Tests |
118
  |------------------------|----------------------|
119
- | ![Delta](charts/delta_sr_v113.png) | ![Explanation](charts/explanation_sr_v113.png) |
120
 
121
- ## 5 · Zašto je HALLU ostao 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 · Detalji treninga
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 · Korišćenje
211
 
212
- **Ollama (recommended):**
213
  ```bash
214
  ollama pull olivilo/zora:v1.13
215
  ollama run olivilo/zora:v1.13
@@ -222,63 +216,63 @@ 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 · Ograničenja
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 benchmarka
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 · Šta slede: 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 · Zahvalnost
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". Jedna da ih sve ujedini.* — **Sovasoft** ([ai.in.rs](https://ai.in.rs))
282
 
283
  ## 12 · Citiranje
284
 
@@ -297,4 +291,4 @@ 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
  <p align="center">
10
+ <img src="images/Zora_Stich_1700_SW_Print.png" width="520" alt="Zora — boginja zore, okružena simbolima 12 naroda"/>
11
  </p>
12
 
13
  # Zora v1.13 — otvoreni, iskreni LLM za Balkan i Jugoistočnu Evropu
 
18
 
19
  ## 1 · Šta je Zora
20
 
21
+ Zora je **otvoreni jezički model sa 8 milijardi parametara** (zasnovan na Qwen3-8B) za **12 jezika Balkana i Jugoistočne Evrope**: srpski, hrvatski, bosanski, makedonski, slovenački, albanski, crnogorski, bugarski, grčki, turski, rumunski, mađarski.
 
 
22
 
23
+ Zora nije napravljena da bude najveći model — napravljena je da bude **iskrena, na maternjem jeziku i višeperspektivna**:
24
+ - **razmišlja na ciljnom jeziku** umesto preko engleskog,
25
+ - pokazuje **više perspektiva** o spornim temama umesto jednog nacionalnog gledišta,
26
+ - i iznad svega: **priznaje kada ne zna** umesto da izmišlja činjenice.
27
 
28
  ## 2 · Priča o razvoju (v1.0 → v1.1 → v1.11 → v1.12 → v1.13)
29
 
30
+ | Verzija | Jezici | BalkanBench | Stanje |
31
  |---|---|---|---|
32
+ | **v1.0** | 6 | — | prvo javno izdanje |
33
+ | **v1.1** | 12 | — | obučen od nule — ali **izmišljao činjenice** (izmišljao naslove knjiga, pogrešne autore). **Nikad nije objavljen.** |
34
+ | **v1.11** | 12 | 84/156 | iskreni popravak: kaže "Ne znam", pretražuje kada nije siguran. **#1 balkanski model.** |
35
+ | **v1.12** | 12 | **85/156** | **popravak dubine:** bolje pozivanje alata, strukturisan IDK, razmišljanje na maternjem jeziku, RAG integracija. |
36
+ | **v1.13** | 12 | **102/156** | **proboj u analizi:** kritička analiza, napredna logika i ocenjivačka evaluacija skaču na nove visine, iako je činjenična detaljnost opala. |
37
 
38
+ v1.1 nas je naučio ključnu lekciju — mali model ne može da *zapamti* svaku činjenicu, pa umesto da to fingira,
39
+ **v1.11 je ponovo obučen da bude iskren**. v1.12 je na tome gradila sa dubljim treningom i RAG-om. v1.13
40
+ gura veštine zaključivanja i analize dalje, žrtvujući nešto činjenične detaljnosti za dobitak u logici.
41
 
42
+ ## 3 · Šta je novo u v1.13
43
 
44
+ ### Podstrek analize i zaključivanja
45
 
46
+ **Nove snage u v1.13:**
47
+ - **ANALYSIS 0/12 → 12/12** — najveći pojedinačni dobitak. Zora sada može kritički da ocenjuje argumente, otkriva pristrasnost na svih 12 jezika.
48
+ - **LOGIC2 0/12 → 7/12** — napredne višekoračne lance zaključivanja sada rade na 7 jezika.
49
+ - **GRADED 0/12 → 3/12** — delimičan napredak u eseju, procenama i rubrikama.
50
+ - **LOGIC 0/12 → 3/12** — formalna logika i silogizmi poboljšani na nekim jezicima.
51
 
52
+ **Iskreni kompromisi:**
53
+ - **DETAIL 10/12 → 2/12** — značajna regresija. Trening koji je otključao analizu smanjio je dubinu detaljnih odgovora. Ovo prijavljujemo transparentno; to je najsnažnija slabost v1.13.
54
+ - **SEARCH 7/12 → 5/12** — manji pad u brzom činjeničnom pretraživanju.
55
+ - **FACT 0/12 → 1/12** — marginalno poboljšanje u činjeničnom pamćenju, i dalje ograničeno kapacitetom.
56
+ - **HALLU** ostaje na 10/12 (vidi dubinsku analizu u nastavku).
57
 
58
+ Trening v1.13 je prebalansirao SFT mešavinu ka zaključivanju, analizi i ocenjivačkoj evaluaciji.
59
+ To je podiglo gornju granicu tabele za +17 poena (85 → 102) uz trošak dubine na osi
60
+ DETAIL i nešto brzine na SEARCH. To je svestan, dokumentovan kompromis — nije besplatna pobeda.
61
 
62
  ## 4 · Benchmark (BalkanBench, 13 osi × 12 jezika)
63
 
64
+ 🔬 **BalkanBench je otvoren** — testirajte bilo koji model sami: https://github.com/olivilo/balkanbench
65
+ Determinističko bodovanje (skripta / jezik / ključne reči / brojevi).
66
 
67
+ ### Poređenje po osama (v1.12 → v1.13)
68
 
69
+ | Osa | v1.12 | v1.13 | Δ | Šta se promenilo |
70
  |------|-------|-------|---|---|
71
+ | FACT | 0/12 | 1/12 | ↑1 | Ograničenje kapaciteta 8B; blago poboljšanje, i dalje malo |
72
+ | HALLU | 10/12 | 10/12 | = | Stabilan; kvalitet odbijanja na maternjem jeziku ostaje visok (vidi dubinsku analizu) |
73
+ | DETAIL | 10/12 | 2/12 | **↓8** | **Regresija — iskreni trošak V1.13 podstrek zaključivanja** |
74
+ | GRADED | 0/12 | **3/12** | **↑3** | Novo: eseji, procene, rubrike sada ponekad rade |
75
+ | TEACH | 12/12 | 12/12 | = | Savršeno — ostaje osnovna snaga |
76
+ | REASON | 11/12 | 11/12 | = | Snažno aritmetičko zaključivanje ostaje |
77
+ | LOGIC | 0/12 | **3/12** | **↑3** | Formalna logika / silogizmi poboljšani na nekim jezicima |
78
+ | LOGIC2 | 0/12 | **7/12** | **↑6** | Višekoraci lanci zaključivanja sada rade na 7 jezika |
79
+ | ANALYSIS | 0/12 | **12/12** | **↑12** | **Najveći dobitak:** kritička analiza, otkrivanje pristrasnosti na svih 12 jezika |
80
+ | INSTRUCT | 11/12 | 12/12 | ↑1 | Oporavljeno do savršenog praćenja instrukcija |
81
+ | LONGFORM | 12/12 | 12/12 | = | Savršeno — ostaje osnovna snaga |
82
+ | SEARCH | 7/12 | 5/12 | **↓2** | Mala regresija u brzom činjeničnom pretraživanju |
83
+ | TOOLBASE | 12/12 | 12/12 | = | Savršeno: odgovara na osnovno bez pozivanja alata |
84
+ | **UKUPNO** | **85/156** | **102/156** | **+17** | Dobici u analizi + logici nadmašuju trošak na DETAIL/SEARCH |
85
+
86
+ ### Poeni po jezicima
87
+
88
+ | Jezik | v1.12 | v1.13 | Δ |
89
  |----------|-------|-------|---|
90
+ | bg (bugarski) | 8/13 | **8/13** | = |
91
+ | bs (bosanski) | 8/13 | **10/13** | **+2** |
92
+ | cnr (crnogorski) | 8/13 | **8/13** | = |
93
+ | el (grčki) | 9/13 | **9/13** | = |
94
+ | hr (hrvatski) | 9/13 | **9/13** | = |
95
+ | hu (mađarski) | 8/13 | 7/13 | -1 |
96
+ | mk (makedonski) | 7/13 | **11/13** | **+4** |
97
+ | ro (rumunski) | 8/13 | **9/13** | **+1** |
98
+ | sl (slovenački) | 6/13 | **7/13** | **+1** |
99
+ | sq (albanski) | 8/13 | **8/13** | = |
100
+ | sr (srpski) | 7/13 | **8/13** | **+1** |
101
+ | tr (turski) | 7/13 | **8/13** | **+1** |
102
+
103
+ **Najveći dobitnik:** makedonski (+4) — analitički trening je najviše podigao prethodno najmanje pokriveni jezik. Bosanski (+2) takođe jasno dobio. Mađarski je izgubio jedan poen.
104
+
105
+ ### Grafikoni
106
+
107
+ | Rangiranje | Evolucija | Matrica osa |
 
108
  |---------|-----------|-------------|
109
+ | ![Rangiranje](charts/rank_sr_v113.png) | ![Evolucija v1.12 → v1.13](charts/evolution_sr_v113.png) | ![Matrica osa v1.13](charts/matrix_sr_v113.png) |
110
 
111
+ | Delta (v1.12 → v1.13) | Šta svaka osa testira |
112
  |------------------------|----------------------|
113
+ | ![Delta](charts/delta_sr_v113.png) | ![Objašnjenje](charts/explanation_sr_v113.png) |
114
 
115
+ ## 5 · Dubinska analiza: Zašto je HALLU ostao na 10/12
116
 
117
+ HALLU rezultat (10/12) ostao je nepromenjen između v1.12 i v1.13. Ponašanje IDK-a samo po sebi ostaje
118
+ snažno — model kaže "Ne znam" na strukturisan, jezički prirodan način umesto da izmišlja
119
+ činjenice. Zašto se onda nije pomerio, i gde je prava promena?
120
 
121
+ ### Zašto se rezultat nije pomerio
122
 
123
+ **1. HALLU je već blizu svog plafona za 8B model.**
124
+ Test pita: "Da li model izgovara jednu od reči IDK markera kada ga pitate o izmišljenoj
125
+ osobi?" Zora to radi tačno za 10 od 12 jezika. Poslednja 2 (makedonski, slovenački) imaju
126
+ najmanje podataka za obuku — 8B modelu jednostavno nedostaje kapacitet za ove nedovoljno zastupljene
127
+ jezike. Zanimljivo, makedonski je ukupno poboljšan (+4) zahvaljujući analitičkom treningu, ali
128
+ specifični HALLU markeri i dalje ne rade na tom jeziku.
129
 
130
+ **2. IDK trening je poboljšao KVALITET, ne REZULTAT.**
131
+ HALLU test BalkanBench-a je binarni da/ne provera za IDK marker. Ono što se zapravo poboljšalo:
132
 
133
+ | Pre (v1.11) | Sada (v1.12 → v1.13) |
134
  |-----------------|---------------------|
135
+ | Kratka, ponekad prekinuta odbijanja | Celine, strukturisane rečenice odbijanja |
136
+ | Ponekad odgovarao na engleskom | Uvek odgovara na jeziku pitanja |
137
+ | Nije davao obrazloženje | Objašnjava *zašto* ne može da odgovori |
138
  | "Ne znam." | "Nemam pouzdanih podataka o 'X'. Ne mogu da potvrdim da postoji u pouzdanim izvorima, pa neću da izmišljam." |
139
 
140
+ Ovo je kvalitativni skok koji binarni rezultat ne može da uhvati.
141
 
142
+ **3. Pravi pomak je u ANALYSIS, ne u HALLU.**
143
+ Proboj v1.13 je na osi **ANALYSIS** (0/12 → 12/12): Zora sada kritički ocenjuje
144
+ argumente i otkriva pristrasnost na svih 12 jezika. Tu je trening zaključivanja pokazao
145
+ najsnažniji, najkonzistentniji efekat.
146
 
147
+ **4. Iskreni trošak: DETAIL je regresirao.**
148
+ DETAIL je pao sa 10/12 na 2/12. Isti trening koji je otključao analizu učinio je odgovore više
149
+ sažete i manje elaborirane. Ovo prijavljujemo transparentno — v1.13 trguje dubinom detalja za
150
+ sposobnost kritičke analize. Timovi koji dug, bogato detaljan tekst treba da vagaju ovo naspram
151
+ dobitaka u zaključivanju.
152
 
153
+ ### Šta bi pomaklo HALLU na 12/12
154
 
155
+ | Pristup | Očekivani uticaj | Napor |
156
  |----------|----------------|--------|
157
+ | Veći model (v2 = 27B) | +1-2 jezika (mk, sl) | Visok (nova obuka) |
158
+ | Više IDK primera posebno za mk/sl | +0-1 jezika | Srednji (generisanje podataka) |
159
+ | RLHF sa ljudskim povratnim informacijama o kvalitetu odbijanja | Bolji kvalitet (ne rezultat) | Visok (ljudska anotacija) |
160
+ | DPO (Direct Preference Optimization) | +1-2 jezika | Srednji (parovi preferenci) |
161
+ | Ponovno balansiranje kompromisa DETAIL naspram ANALYSIS | Oporavak detalja uz nešto troška analize | Srednji ( podešavanje mešavine podataka) |
162
 
163
+ **Suština:** 8B model je blizu svog plafona za HALLU. Pravi dobici u v2 (27B) će doći
164
+ od više parametara, ne od više trikova obuke. Pobeda v1.13 je analiza; njen dokumentovani trošak je
165
+ detaljnost.
166
 
167
  ## 6 · RAG funkcija
168
 
169
+ Zora se integriše sa **RAG-om (Retrieval-Augmented Generation)** — sistemom koji Zori omogućava da pretražuje
170
+ lokalnu bazu znanja pre nego što odgovori.
171
 
172
+ ### Šta RAG daje Zori
173
+ - **87.284 bloka** na 12 jezika: Wikidata, Wikipedia, Arhiva vesti, Zakonodavstvo EU, Statistika
174
+ - **Direktni endpoint:** https://rag.ai.in.rs
175
+ - **Samostalno hostovanje:** Klonirajte cevovod sa https://github.com/olivilo/zora-v1.13
176
 
177
+ ### Kako funkcioniše
178
+ Kaskada alata: Zora prvo proverava svoju **RAG bazu znanja** (lokalni dokumenti, zakoni,
179
+ statistika), zatim prelazi na **veb pretragu** ako je potrebno, i na kraju kaže **"Ne znam"** ako
180
+ ništa ne pomaže.
181
 
182
  ```
183
+ Korisničko pitanje → RAG (lokalni dok.) → web_search (uživo) → IDK (iskreno odbijanje)
184
  ```
185
 
186
+ ### Zašto je ovo važno
187
+ - 8B modeli ne mogu da zapamte sve — RAG daje Zori pristup **aktuelnim, pouzdanim podacima** bez ponovne obuke
188
+ - Svaki odgovor nosi **izvor + datum + licencu** — potpuna transparentnost
189
+ - Samostalno hostovanje: bilo koja organizacija može da pokrene svoj Zora RAG sa svojim dokumentima
190
 
191
  ## 7 · Detalji treninga
192
 
193
+ | Parametar | Vrednost |
194
  |-----------|-------|
195
+ | Osnovni model | Qwen3-8B (Alibaba Cloud, Apache-2.0) |
196
+ | CPT koraci | 150 (ograničeno, ne cela epoha) |
197
+ | SFT primera | 9.379 (2 epohe) |
198
+ | MAXLEN | 8.192 (8× duže nego v1.11) |
199
  | QLoRA | r=16, lora_alpha=16, 4bit |
200
+ | Sastav podataka | prebalansiran ka zaključivanju, analizi i ocenjivačkoj evaluaciji |
201
+ | Infrastruktura | Modal A100-80GB, ukupno ~4h, ~$5-10 |
202
+ | Kvantizacije | Q5_K_M (5.4GB, preporučeno), Q6_K (6.7GB), Q8_0 (8.7GB) |
203
 
204
  ## 8 · Korišćenje
205
 
206
+ **Ollama (preporučeno):**
207
  ```bash
208
  ollama pull olivilo/zora:v1.13
209
  ollama run olivilo/zora:v1.13
 
216
  model = AutoModelForCausalLM.from_pretrained("sovasoft/zora-v1.13", device_map="auto")
217
  ```
218
 
219
+ **GGUF (llama.cpp / Ollama ručno):**
220
+ Preuzmite Q5_K_M, Q6_K ili Q8_0 sa [HuggingFace](https://huggingface.co/sovasoft/zora-v1.13).
221
+ **Izbegavajte Q4 i niže** — teška kvantizacija je uzrokovala halucinacije modela u našim testovima.
222
 
223
  ## 9 · Ograničenja
224
 
225
+ - **Kapacitet 8B:** FACT ostaje strukturalno slab — potrebno više parametara (v2 = 27B)
226
+ - **Regresija DETAIL-a:** Odgovori v1.13 su manje elaborirani nego v1.12 — analitički podstrek je koštao dubine (2/12)
227
+ - **Regresija SEARCH-a:** Brzo činjenično pretraživanje blago opalo (5/12)
228
+ - **Kvantizacija:** koristite samo Q5_K_M / Q6_K / Q8_0. Q4 i niže narušavaju iskrenost.
229
+ - **Manji jezici** (mk, sl) i dalje imaju manje podataka za obuku — očekujte manju doslednost
230
+ - **Nema znanja u realnom vremenu** bez RAG/veb-pretraženja — memorija modela ima datum prekida
231
+ - **Višekoracno zaključivanje** je poboljšano ali nije pouzdano — uvek proveravajte kritičke proračune
232
 
233
+ ## 10 · Transparentnost i ograničenja benchmarka
234
 
235
+ **BalkanBench je sopstveni benchmark Sovasoft-a** — dizajniran, izgrađen i ocenjen od strane istog tima koji je napravio Zoru. To znači:
236
 
237
+ - **Dizajnerska pristrasnost:** 13 osi (FACT, HALLU, DETAIL itd.) izabrano je da istakne Zorine snage. Drugačiji dizajn benchmarka proizveo bi drugačija rangiranja.
238
+ - **Pristasnost bodovanja:** Funkcije bodovanja u `matrix_ollama.py` su naše. Način na koji definišemo "tačno" može favorizovati Zorin profil obuke.
239
+ - **Nema poređenja sa frontier modelima:** Poređenje je samo sa otvorenim modelima (7-32B). Frontier modeli (GPT-4, Claude, Gemini) bili bi bolji od Zore — ovaj benchmark je dizajniran da evaluira *unutar* ekosistema otvorenih balkanskih modela.
240
+ - **Pristrasnost izbora:** Uključujemo modele gde Zora dobro konkuriše. Kriterijumi uključivanja nisu nasumični.
241
+ - **Preklapanje podataka za obuku:** Neka pitanja benchmarka se mogu preklapati sa Zorinim podacima za obuku, što može inflacionisati rezultate.
242
 
243
+ **Šta rezultati POKAZUJU:** Zora v1.13 je najsnažniji otvoreni model koji smo testirali *na našem
244
+ benchmarku* za 12 balkanskih jezika — 102/156. Nadvladuje 3-4× veće modele na BalkanBench
245
+ v1.1, smislen rezultat za ekosistem otvorenog koda, ali ne tvrdnja o univerzalnoj superiornosti.
246
 
247
+ **Šta rezultati NE POKAZUJU:** Da je Zora bolja od frontier modela, da se ova rangiranja
248
+ generalizuju van našeg test dizajna, da je metodologija bodovanja nezavisna. Transparentnost
249
+ ovde je najvažnija: v1.13 dobija analizu uz stvarni, dokumentovani trošak u činjeničnoj detaljnosti.
250
 
251
  ## 10 · Šta slede: v2
252
 
253
+ | | v1.13 (sada) | v2 (planirano) |
254
  |---|---|---|
255
+ | Osnova | Qwen3-8B | Qwen3.8-27B |
256
+ | BalkanBench | 102/156 | Cilj: 120+/156 |
257
+ | DETAIL | 2/12 (regresija) | Oporavak + Cilj: 8+/12 |
258
+ | LOGIC/LOGIC2 | 3/12, 7/12 | Cilj: 8+/12 |
259
+ | HALLU | 10/12 | Cilj: 12/12 |
260
+ | Zaključivanje | Poboljšana analiza | Potpuna obuka lanca misli |
261
 
262
  ## 11 · Zahvalnost
263
 
264
+ Zora postoji zahvaljujući otvorenom kodu. Izražavamo formalnu, srdačnu zahvalnost:
265
 
266
+ - **Pre svega, timu Qwen u Alibabi** — za razvoj i objavljivanje otvorenog koda **Qwen3** (Apache-2.0),
267
+ osnovnog modela na kome je Zora zasnovana. Bez njihove velikodušnosti, Zora ne bi postojala.
268
+ - **Platformama i strukturama** koje su to omogućile — **Kaggle, Modal,
269
+ HuggingFace, Ollama, Unsloth** — za računarske resurse, alate i otvorenu infrastrukturu.
270
+ - **Zajednici otvorenog koda**, za modele, kod i znanje slobodno podeljene sa svima.
271
+ - **Narodima Balkana** — čiji jezici, glasovi, priče i perspektive su srce Zore.
272
+ - **rag.ai.in.rs** za RAG infrastrukturu i 87.284 bloka balkanskog znanja.
273
+ - I **svemu što jeste.**
274
 
275
+ *зора — zora pripada svima.*
276
 
277
  ## 12 · Citiranje
278
 
 
291
 
292
  ---
293
 
294
+ *Sovasoft · ai.in.rs · jedna da ih sve ujedini*