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

Libraries

How to use texdata/Qwen3.6-35B-A3B-Tool-Slo-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-Tool-Slo-GGUF",
	filename="mmproj-qwen3.6-35b-a3b-tool-slo-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-Tool-Slo-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-Tool-Slo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf texdata/Qwen3.6-35B-A3B-Tool-Slo-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-Tool-Slo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf texdata/Qwen3.6-35B-A3B-Tool-Slo-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-Tool-Slo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf texdata/Qwen3.6-35B-A3B-Tool-Slo-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-Tool-Slo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf texdata/Qwen3.6-35B-A3B-Tool-Slo-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use texdata/Qwen3.6-35B-A3B-Tool-Slo-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-Tool-Slo-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-Tool-Slo-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

How to use texdata/Qwen3.6-35B-A3B-Tool-Slo-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-Tool-Slo-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-Tool-Slo-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use texdata/Qwen3.6-35B-A3B-Tool-Slo-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-Tool-Slo-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-Tool-Slo-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use texdata/Qwen3.6-35B-A3B-Tool-Slo-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-Tool-Slo-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-Tool-Slo-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-Tool-Slo-GGUF with Docker Model Runner:
```
docker model run hf.co/texdata/Qwen3.6-35B-A3B-Tool-Slo-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Qwen3.6-35B-A3B Tool Slo (GGUF)

Slovenski pogovorni / glasovni model z zanesljivim, izboljšanim klicanjem orodij (function / tool calling), grajen na Qwen3.6-35B-A3B.

Dodatno SFT fine-tunan za multi-turn agentske tool-calling delotoke, na:

Salesforce / APIGen-MT-5k — multi-turn agentic (tau-bench slog)
Team-ACE / ToolACE — raznolik function-calling
NousResearch Hermes function-calling

Cilj: zanesljivo native function-calling skozi cel pogovor (ne le v prvi izmenjavi).

Datoteke

kvant	velikost	opomba
`qwen3.6-35b-a3b-tool-slo-Q4_K_M.gguf`	~21 GB	hitrost / manj VRAM
`qwen3.6-35b-a3b-tool-slo-Q8_0.gguf`	~36 GB	kvaliteta
`mmproj-qwen3.6-35b-a3b-tool-slo-F16.gguf`	~0.9 GB	vision projektor

Native tool-calling v Qwen3.6 XML formatu (<tool_call><function=...><parameter=...></parameter></function></tool_call>)
MTP ohranjen; kontekst do 256k
Del lokalnega slovenskega glasovnega + vizualnega asistenta (ASR → LLM → TTS)

Zagon (llama.cpp / LM Studio)

llama-server -m qwen3.6-35b-a3b-tool-slo-Q4_K_M.gguf \
  --mmproj mmproj-qwen3.6-35b-a3b-tool-slo-F16.gguf \
  --jinja -ngl 99 -c 40960

--jinja vklopi tool-calling prek vgrajenega chat templata.

Downloads last month: 73

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

8-bit

Model tree for texdata/Qwen3.6-35B-A3B-Tool-Slo-GGUF

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