Instructions to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="texdata/Qwen3.6-35B-A3B-Slovenian-GGUF",
	filename="mmproj-qwen3.6-35b-a3b-slovenian-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF 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 texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_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 texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_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 texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M

Use Docker

docker model run hf.co/texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "texdata/Qwen3.6-35B-A3B-Slovenian-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "texdata/Qwen3.6-35B-A3B-Slovenian-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M

Ollama
How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with Ollama:
```
ollama run hf.co/texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M
```

Unsloth Studio

How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF 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 texdata/Qwen3.6-35B-A3B-Slovenian-GGUF 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 texdata/Qwen3.6-35B-A3B-Slovenian-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for texdata/Qwen3.6-35B-A3B-Slovenian-GGUF to start chatting

How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_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": "texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_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 texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_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 "texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_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 texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with Docker Model Runner:
```
docker model run hf.co/texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M
```

Lemonade

How to use texdata/Qwen3.6-35B-A3B-Slovenian-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull texdata/Qwen3.6-35B-A3B-Slovenian-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Slovenian-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-35B-A3B — Slovenian (GGUF)

Qwen/Qwen3.6-35B-A3B continued-pretrained + SFT for Slovenian (chat, knowledge, en↔sl translation). Reasoning model. GGUF for LM Studio / llama.cpp.

On held-out evals vs the untuned base: Slovenian-LLM-Eval acc_norm 0.623 → 0.654; translation BLEU en→sl 23.8 → 26.3, sl→en 30.9 → 35.0.

Files

File	Size	Use
`qwen3.6-35b-a3b-slovenian-Q4_K_M.gguf`	~21 GB	recommended
`qwen3.6-35b-a3b-slovenian-Q8_0.gguf`	~38 GB	best quality
`mmproj-qwen3.6-35b-a3b-slovenian-F16.gguf`	~0.9 GB	vision (load alongside for image input)

How to run

LM Studio: put the folder under ~/.lmstudio/models/<you>/, load the model.

Enable Reasoning/Thinking in the model settings (it's a reasoning model).
Image input: LM Studio auto-pairs the mmproj file.

llama.cpp:

llama-cli -m qwen3.6-35b-a3b-slovenian-Q4_K_M.gguf -p "Prevedi v slovenščino: Good morning!"
# vision:
llama-mtmd-cli -m ...-Q4_K_M.gguf --mmproj mmproj-...-F16.gguf --image slika.jpg -p "Opiši sliko."

License & data provenance

Base Qwen/Qwen3.6-35B-A3B is Apache-2.0. Training data (mixed → this repo is license: other):

Data	License
Slovenian Wikipedia (CPT)	CC BY-SA 4.0 (attribution + ShareAlike)
FineWeb2 sl (CPT)	ODC-BY 1.0
`cjvt/GaMS-Nemotron-Chat` (SFT)	no explicit license — derived from LMSYS-Chat-1M (custom terms) + NVIDIA Nemotron PT
OPUS-100 en–sl (SFT)	unknown / mixed

⚠️ The LMSYS-Chat-1M and OPUS-100 terms are unresolved. Resolve them and pick a final license before public or commercial use.

Downloads last month: 919

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

8-bit

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