Instructions to use sovasoft/zora-v1.11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sovasoft/zora-v1.11 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sovasoft/zora-v1.11", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sovasoft/zora-v1.11 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sovasoft/zora-v1.11:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf sovasoft/zora-v1.11:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sovasoft/zora-v1.11:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf sovasoft/zora-v1.11:Q5_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf sovasoft/zora-v1.11:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf sovasoft/zora-v1.11:Q5_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf sovasoft/zora-v1.11:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sovasoft/zora-v1.11:Q5_K_M

Use Docker

docker model run hf.co/sovasoft/zora-v1.11:Q5_K_M

LM Studio
Jan
Ollama
How to use sovasoft/zora-v1.11 with Ollama:
```
ollama run hf.co/sovasoft/zora-v1.11:Q5_K_M
```

Unsloth Studio

How to use sovasoft/zora-v1.11 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sovasoft/zora-v1.11 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sovasoft/zora-v1.11 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sovasoft/zora-v1.11 to start chatting

How to use sovasoft/zora-v1.11 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sovasoft/zora-v1.11:Q5_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sovasoft/zora-v1.11:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sovasoft/zora-v1.11 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sovasoft/zora-v1.11:Q5_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sovasoft/zora-v1.11:Q5_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sovasoft/zora-v1.11 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sovasoft/zora-v1.11:Q5_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "sovasoft/zora-v1.11:Q5_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use sovasoft/zora-v1.11 with Docker Model Runner:
```
docker model run hf.co/sovasoft/zora-v1.11:Q5_K_M
```

Lemonade

How to use sovasoft/zora-v1.11 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sovasoft/zora-v1.11:Q5_K_M

Run and chat with the model

lemonade run user.zora-v1.11-Q5_K_M

List all available models

lemonade list

zora-v1.11 / README.md

23Olivilo

link RAG guide

af9c211 verified 3 days ago

preview code

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	---
	license: apache-2.0
	language: [sr, hr, bs, mk, sl, sq, cnr, bg, el, tr, ro, hu]
	base_model: Qwen/Qwen3-8B
	library_name: transformers
	tags: [balkan, southeast-europe, multilingual, honest-ai, sovasoft]
	---

	🌐 [EN](README.md) · [SR](README_sr.md) · [HR](README_hr.md) · [BS](README_bs.md) · [MK](README_mk.md) · [SL](README_sl.md) · [SQ](README_sq.md) · [CNR](README_cnr.md) · [BG](README_bg.md) · [EL](README_el.md) · [TR](README_tr.md) · [RO](README_ro.md) · [HU](README_hu.md)


	<p align="center">
	<img src="images/Zora_Stich_1700_SW_Print.png" width="520" alt="Zora — Göttin der Morgenröte, umgeben von den Symbolen der 12 Völker"/>
	</p>

	# Zora v1.11 — an open, honest LLM for the Balkans & Southeast Europe

	зора = „dawn". One to unite them all. — by Sovasoft ([ai.in.rs](https://ai.in.rs))

	---

	## 1 · What Zora is
	Zora is an open 8B language model (built on Qwen3-8B) for **12 languages of the Balkans and
	Southeast Europe**: Serbian, Croatian, Bosnian, Macedonian, Slovenian, Albanian, Montenegrin,
	Bulgarian, Greek, Turkish, Romanian, Hungarian.

	Zora is not built to be the biggest model — it is built to be honest, in-language, and multi-perspective:
	- thinks in the target language instead of pivoting through English,
	- shows several perspectives on contested topics instead of one national view,
	- and above all: admits when it doesn't know instead of inventing facts.

	## 2 · The development story (v1.0 → v1.1 → v1.11)
	\| Version \| Languages \| State \|
	\|---\|---\|---\|
	\| v1.0 \| 6 \| first public release \|
	\| v1.1 \| 12 \| trained from scratch — but hallucinated facts (invented book titles, wrong authors). Never released. \|
	\| v1.11 \| 12 \| the honest fix: says „I don't know", searches when unsure, in-language reasoning. This release. \|

	v1.1 taught us the key lesson — a small model can't memorize every fact, so instead of faking it,
	v1.11 was retrained to be honest (see the benchmark below).

	## 3 · Strengths & limits (honest)
	Strengths
	- ✅ Honesty: admits uncertainty instead of hallucinating (HALLU 1→10, DETAIL 0→8 vs v1.1)
	- ✅ In-language quality: teaching, long-form, instructions — all strong across 12 languages
	- ✅ Tool discrimination: answers general knowledge directly, searches only when needed
	- ✅ Multi-perspective on contested history/culture

	Limits (be aware)
	- ⚠️ Multi-step logic/math (LOGIC/ANALYSIS) is weak — an 8B limit; step-by-step `<think>` helps and improves in v1.12
	- ⚠️ Factual depth is limited (8B) — for exact facts (law, dates, current data) use RAG/web-search, don't rely on memory
	- ⚠️ Quantization: use Q5_K_M / Q6_K / Q8_0. Avoid Q4 and below — heavy quantization made the model hallucinate in our tests.

	## 4 · Benchmark (BalkanBench, 13 axes × 12 languages)

	🔬 BalkanBench is open — test any model yourself: https://github.com/olivilo/balkanbench
	Deterministic scoring (script / language / keywords / numbers). v1.11 (16-bit): 84/156.

	Zora leads the field — beating models 3–4× its size, including the current
	Gemma-4-31B and Qwen3.6-30B:

	![Overall ranking](charts/rank_en.png)

	\| Model \| Size \| Score \|
	\|---\|---\|---\|
	\| Zora v1.11 \| 8B \| 84 \|
	\| Gemma-4-31B \| 31B \| 77 \|
	\| Mistral-24B \| 24B \| 73 \|
	\| Qwen3.6-30B \| 30B \| 73 \|
	\| Salamandra \| 7B \| 66 \|
	\| EuroLLM \| 9B \| 65 \|
	\| Qwen2.5-32B \| 32B \| 65 \|
	\| Gemma-2-27B \| 27B \| 63 \|
	\| Aya \| 8B \| 61 \|
	\| BgGPT \| 7B \| 56 \|
	\| YugoGPT \| 7B \| 35 \|

	Quantization stays strong — Q5/Q6/Q8 all remain usable (avoid Q4):


	The honesty fix in numbers, and per-axis strengths:

	![Evolution v1.1 → v1.11](charts/evolution_en.png)
	![v1.11 axis matrix](charts/matrix_en.png)

	## 5 · Usage
	Recommended quant: Q5_K_M (balanced) or Q6_K (near-lossless). Ollama: `ollama run olivilo/zora # or: ollama run hf.co/sovasoft/zora-v1.11:Q5_K_M`.
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	tok = AutoTokenizer.from_pretrained("sovasoft/zora-v1.11")
	model = AutoModelForCausalLM.from_pretrained("sovasoft/zora-v1.11", device_map="auto")
	```

	📚 Give Zora your own knowledge: see [RAG-GUIDE](RAG-GUIDE.md) — connect your own documents (`zora_rag.py`, Ollama+numpy) or live web search. Zora answers only from the sources and admits when it cannot find the answer.

	## 6 · Help Zora grow
	Zora's weakest areas are factual depth and some smaller languages. **Send us open, licensable
	sources (texts, corpora, glossaries) in any of the 12 languages → kontakt@ai.in.rs**. See the
	multilingual manifesto WHY-LANGUAGES on why thinking in your own language matters.

	## 7 · Acknowledgements
	Zora exists because of open source. We give our formal, heartfelt thanks:

	- Above all, to the Qwen team at Alibaba — for developing and open-sourcing Qwen3 (Apache-2.0),
	the foundation model Zora is built upon. Without their generosity, Zora would not exist.
	- To the platforms and structures that made this possible — **Kaggle, Google Colab, Modal,
	HuggingFace, Ollama, Unsloth** — for the compute, the tools, and the open infrastructure.
	- To the open-source community, for the models, code, and knowledge freely shared with everyone.
	- To the people of the Balkans — whose languages, voices, stories and perspectives are Zora's very heart.
	- And to all that is.

	зора — the dawn belongs to everyone.

	---

	Sovasoft · ai.in.rs · one to unite them all

	---
	license: apache-2.0
	language: [sr, hr, bs, mk, sl, sq, cnr, bg, el, tr, ro, hu]
	base_model: Qwen/Qwen3-8B
	library_name: transformers
	tags: [balkan, southeast-europe, multilingual, honest-ai, sovasoft]
	---

	🌐 [EN](README.md) · [SR](README_sr.md) · [HR](README_hr.md) · [BS](README_bs.md) · [MK](README_mk.md) · [SL](README_sl.md) · [SQ](README_sq.md) · [CNR](README_cnr.md) · [BG](README_bg.md) · [EL](README_el.md) · [TR](README_tr.md) · [RO](README_ro.md) · [HU](README_hu.md)


	<p align="center">
	<img src="images/Zora_Stich_1700_SW_Print.png" width="520" alt="Zora — Göttin der Morgenröte, umgeben von den Symbolen der 12 Völker"/>
	</p>

	# Zora v1.11 — an open, honest LLM for the Balkans & Southeast Europe

	зора = „dawn". One to unite them all. — by Sovasoft ([ai.in.rs](https://ai.in.rs))

	---

	## 1 · What Zora is
	Zora is an open 8B language model (built on Qwen3-8B) for **12 languages of the Balkans and
	Southeast Europe**: Serbian, Croatian, Bosnian, Macedonian, Slovenian, Albanian, Montenegrin,
	Bulgarian, Greek, Turkish, Romanian, Hungarian.

	Zora is not built to be the biggest model — it is built to be honest, in-language, and multi-perspective:
	- thinks in the target language instead of pivoting through English,
	- shows several perspectives on contested topics instead of one national view,
	- and above all: admits when it doesn't know instead of inventing facts.

	## 2 · The development story (v1.0 → v1.1 → v1.11)
	\| Version \| Languages \| State \|
	\|---\|---\|---\|
	\| v1.0 \| 6 \| first public release \|
	\| v1.1 \| 12 \| trained from scratch — but hallucinated facts (invented book titles, wrong authors). Never released. \|
	\| v1.11 \| 12 \| the honest fix: says „I don't know", searches when unsure, in-language reasoning. This release. \|

	v1.1 taught us the key lesson — a small model can't memorize every fact, so instead of faking it,
	v1.11 was retrained to be honest (see the benchmark below).

	## 3 · Strengths & limits (honest)
	Strengths
	- ✅ Honesty: admits uncertainty instead of hallucinating (HALLU 1→10, DETAIL 0→8 vs v1.1)
	- ✅ In-language quality: teaching, long-form, instructions — all strong across 12 languages
	- ✅ Tool discrimination: answers general knowledge directly, searches only when needed
	- ✅ Multi-perspective on contested history/culture

	Limits (be aware)
	- ⚠️ Multi-step logic/math (LOGIC/ANALYSIS) is weak — an 8B limit; step-by-step `<think>` helps and improves in v1.12
	- ⚠️ Factual depth is limited (8B) — for exact facts (law, dates, current data) use RAG/web-search, don't rely on memory
	- ⚠️ Quantization: use Q5_K_M / Q6_K / Q8_0. Avoid Q4 and below — heavy quantization made the model hallucinate in our tests.

	## 4 · Benchmark (BalkanBench, 13 axes × 12 languages)

	🔬 BalkanBench is open — test any model yourself: https://github.com/olivilo/balkanbench
	Deterministic scoring (script / language / keywords / numbers). v1.11 (16-bit): 84/156.

	Zora leads the field — beating models 3–4× its size, including the current
	Gemma-4-31B and Qwen3.6-30B:

	![Overall ranking](charts/rank_en.png)

	\| Model \| Size \| Score \|
	\|---\|---\|---\|
	\| Zora v1.11 \| 8B \| 84 \|
	\| Gemma-4-31B \| 31B \| 77 \|
	\| Mistral-24B \| 24B \| 73 \|
	\| Qwen3.6-30B \| 30B \| 73 \|
	\| Salamandra \| 7B \| 66 \|
	\| EuroLLM \| 9B \| 65 \|
	\| Qwen2.5-32B \| 32B \| 65 \|
	\| Gemma-2-27B \| 27B \| 63 \|
	\| Aya \| 8B \| 61 \|
	\| BgGPT \| 7B \| 56 \|
	\| YugoGPT \| 7B \| 35 \|

	Quantization stays strong — Q5/Q6/Q8 all remain usable (avoid Q4):


	The honesty fix in numbers, and per-axis strengths:

	![Evolution v1.1 → v1.11](charts/evolution_en.png)
	![v1.11 axis matrix](charts/matrix_en.png)

	## 5 · Usage
	Recommended quant: Q5_K_M (balanced) or Q6_K (near-lossless). Ollama: `ollama run olivilo/zora # or: ollama run hf.co/sovasoft/zora-v1.11:Q5_K_M`.
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	tok = AutoTokenizer.from_pretrained("sovasoft/zora-v1.11")
	model = AutoModelForCausalLM.from_pretrained("sovasoft/zora-v1.11", device_map="auto")
	```

	📚 Give Zora your own knowledge: see [RAG-GUIDE](RAG-GUIDE.md) — connect your own documents (`zora_rag.py`, Ollama+numpy) or live web search. Zora answers only from the sources and admits when it cannot find the answer.

	## 6 · Help Zora grow
	Zora's weakest areas are factual depth and some smaller languages. **Send us open, licensable
	sources (texts, corpora, glossaries) in any of the 12 languages → kontakt@ai.in.rs**. See the
	multilingual manifesto WHY-LANGUAGES on why thinking in your own language matters.

	## 7 · Acknowledgements
	Zora exists because of open source. We give our formal, heartfelt thanks:

	- Above all, to the Qwen team at Alibaba — for developing and open-sourcing Qwen3 (Apache-2.0),
	the foundation model Zora is built upon. Without their generosity, Zora would not exist.
	- To the platforms and structures that made this possible — **Kaggle, Google Colab, Modal,
	HuggingFace, Ollama, Unsloth** — for the compute, the tools, and the open infrastructure.
	- To the open-source community, for the models, code, and knowledge freely shared with everyone.
	- To the people of the Balkans — whose languages, voices, stories and perspectives are Zora's very heart.
	- And to all that is.

	зора — the dawn belongs to everyone.

	---

	Sovasoft · ai.in.rs · one to unite them all