Instructions to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF",
	filename="Qwen3.6-35B-A3B-REAM-192-Q3_K_S.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama-cli -hf keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama-cli -hf keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

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 keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S
# Run inference directly in the terminal:
./llama-cli -hf keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

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 keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

Use Docker

docker model run hf.co/keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

LM Studio
Jan

vLLM

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keithnull/Qwen3.6-35B-A3B-REAM-192-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": "keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

Ollama
How to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with Ollama:
```
ollama run hf.co/keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S
```

Unsloth Studio

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-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 keithnull/Qwen3.6-35B-A3B-REAM-192-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 keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF to start chatting

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

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": "keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

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 keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

Run Hermes

hermes

Docker Model Runner
How to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with Docker Model Runner:
```
docker model run hf.co/keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S
```

Lemonade

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull keithnull/Qwen3.6-35B-A3B-REAM-192-GGUF:Q3_K_S

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-REAM-192-GGUF-Q3_K_S

List all available models

lemonade list

Reduce more memory usage

by Kz000000 - opened 23 days ago

Discussion

Kz000000

23 days ago

Should do apex quant for this https://github.com/mudler/apex-quant , would reduce more VRAM usage without losing as much quality as q3 for example

keithnull

Owner 23 days ago

Yeah, that would be cool. I'll do an Apex quant and some comparison benchmarks when I have time.

keithnull

Owner 23 days ago

https://huggingface.co/keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF

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