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mradermacher
/
Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF

Text Generation
Transformers
GGUF
MLX
coding
research
unsloth
qwen3_5
qwen3_6
qwen
fable
qwen3.6
qwen3.5
claude4.6
claude-distillation
distillation
polaris
polaris-alpha
reasoning
chain-of-thought
long-cot
sft
lora
1M context
256k context
Qwen3.6
All use cases
instruction-tuned
conversational
multilingual
math
stem
experimental
Deckard(qx)
creative
creative writing
fiction writing
plot generation
sub-plot generation
story generation
scene continue
storytelling
fiction story
science fiction
all genres
story
writing
vivid prosing
vivid writing
fiction
bf16
roleplaying
mergekit
Merge
Model card Files Files and versions
xet
Community

Instructions to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF", device_map="auto")
  • MLX

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with MLX:

    # Make sure mlx-lm is installed
    # pip install --upgrade mlx-lm
    
    # Generate text with mlx-lm
    from mlx_lm import load, generate
    
    model, tokenizer = load("mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF")
    
    prompt = "Write a story about Einstein"
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )
    
    text = generate(model, tokenizer, prompt=prompt, verbose=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-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 mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-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 mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-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 mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-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 mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
    Use Docker
    docker model run hf.co/mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
  • LM Studio
  • Jan
  • vLLM

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-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": "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
  • SGLang

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Ollama

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with Ollama:

    ollama run hf.co/mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
  • Unsloth Desktop
  • Pi

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with Pi:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF"
    Configure the model in Pi
    # Install Pi:
    npm install -g @earendil-works/pi-coding-agent
    # Add to ~/.pi/agent/models.json:
    {
      "providers": {
        "mlx-lm": {
          "baseUrl": "http://localhost:8080/v1",
          "api": "openai-completions",
          "apiKey": "none",
          "models": [
            {
              "id": "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • MLX LM

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with MLX LM:

    Generate or start a chat session
    # Install MLX LM
    uv tool install mlx-lm
    # Interactive chat REPL
    mlx_lm.chat --model "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF"
    Run an OpenAI-compatible server
    # Install MLX LM
    uv tool install mlx-lm
    # Start the server
    mlx_lm.server --model "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF"
    # Calling the OpenAI-compatible server with curl
    curl -X POST "http://localhost:8000/v1/chat/completions" \
       -H "Content-Type: application/json" \
       --data '{
         "model": "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF",
         "messages": [
           {"role": "user", "content": "Hello"}
         ]
       }'
  • Docker Model Runner

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with Docker Model Runner:

    docker model run hf.co/mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
  • Lemonade

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF:Q4_K_M
    Run and chat with the model
    lemonade run user.Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF-Q4_K_M
    List all available models
    lemonade list
  • Hermes Agent

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with Hermes Agent:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF"
    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 mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF
    Run Hermes
    hermes
  • Atomic Chat
  • OpenClaw

    How to use mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF with OpenClaw:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF"
    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 "mradermacher/Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF" \
      --custom-provider-id mlx-lm \
      --custom-compatibility openai \
      --custom-text-input \
      --accept-risk \
      --skip-health
    Run OpenClaw
    openclaw agent --local --agent main --message "Hello from Hugging Face"
Qwen3.8-27B-Brainwaves-WFH-BF16-GGUF
186 GB
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History: 19 commits
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