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agentionai
/
Qwen3.8-Flash-Next-Gyro-GGUF

Image-Text-to-Text
GGUF
qwen3.8-flash-next
qwen4exp
experimental
agentionai
conversational
Model card Files Files and versions
xet
Community
8

Instructions to use agentionai/Qwen3.8-Flash-Next-Gyro-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use agentionai/Qwen3.8-Flash-Next-Gyro-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 agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    # Run inference directly in the terminal:
    llama cli -hf agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    # Run inference directly in the terminal:
    llama cli -hf agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    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 agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    # Run inference directly in the terminal:
    ./llama-cli -hf agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    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 agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    Use Docker
    docker model run hf.co/agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
  • Llama new
  • LM Studio
  • Jan
  • vLLM

    How to use agentionai/Qwen3.8-Flash-Next-Gyro-GGUF with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "agentionai/Qwen3.8-Flash-Next-Gyro-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": "agentionai/Qwen3.8-Flash-Next-Gyro-GGUF",
    		"messages": [
    			{
    				"role": "user",
    				"content": [
    					{
    						"type": "text",
    						"text": "Describe this image in one sentence."
    					},
    					{
    						"type": "image_url",
    						"image_url": {
    							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
    						}
    					}
    				]
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
  • Ollama

    How to use agentionai/Qwen3.8-Flash-Next-Gyro-GGUF with Ollama:

    ollama run hf.co/agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
  • Unsloth Desktop
  • Pi

    How to use agentionai/Qwen3.8-Flash-Next-Gyro-GGUF with Pi:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    Configure the model in Pi
    # Install Pi:
    npm install -g @earendil-works/pi-coding-agent
    # Add to ~/.pi/agent/models.json:
    {
      "providers": {
        "llama-cpp": {
          "baseUrl": "http://localhost:8080/v1",
          "api": "openai-completions",
          "apiKey": "none",
          "models": [
            {
              "id": "agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Docker Model Runner

    How to use agentionai/Qwen3.8-Flash-Next-Gyro-GGUF with Docker Model Runner:

    docker model run hf.co/agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
  • Lemonade

    How to use agentionai/Qwen3.8-Flash-Next-Gyro-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    Run and chat with the model
    lemonade run user.Qwen3.8-Flash-Next-Gyro-GGUF-TQ2_0
    List all available models
    lemonade list
  • Hermes Agent

    How to use agentionai/Qwen3.8-Flash-Next-Gyro-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 agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    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 agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    Run Hermes
    hermes
  • Atomic Chat
  • OpenClaw

    How to use agentionai/Qwen3.8-Flash-Next-Gyro-GGUF with OpenClaw:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0
    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 "agentionai/Qwen3.8-Flash-Next-Gyro-GGUF:TQ2_0" \
      --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"
Qwen3.8-Flash-Next-Gyro-GGUF
262 GB
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  • 1 contributor
History: 47 commits
agentionai's picture
agentionai
Card: Strata link to main; RTX 5090 Gyro-L community report (discussion #6)
0589275 verified 1 day ago
  • .gitattributes
    2.26 kB
    Add Gyro-L (64 GB tier): TQP pair-trellis experts, 50.2 GiB GPU weights 15 days ago
  • LICENSE
    3.24 kB
    Qwen3.8-Flash-Next Gyro (experimental): 32 GB rotor quant 27 days ago
  • Qwen3.8-Flash-Next-Gyro-L-TQ3_0.gguf
    108 GB
    xet
    Gyro-L: 8-bit n-gram table (same GPU weights, 50.2 GiB) 14 days ago
  • Qwen3.8-Flash-Next-Gyro-M-TQ2_0.gguf
    92 GB
    xet
    Gyro-M update: improved encode (KLD 0.291) 16 days ago
  • Qwen3.8-Flash-Next-Gyro-S-TQ1_0.gguf
    58.5 GB
    xet
    Gyro-S update: improved encode (KLD 0.401) 17 days ago
  • README.md
    14.1 kB
    Card: Strata link to main; RTX 5090 Gyro-L community report (discussion #6) 1 day ago
  • gyro-header.png
    562 kB
    xet
    Card header image 22 days ago
  • mmproj-F16.gguf
    904 MB
    xet
    Add the vision projector; MTP draft renamed to mtp-* so -hf finds it 26 days ago
  • mtp-Qwen3.8-Flash-Next-draft.gguf
    2.7 GB
    xet
    MTP draft: upstream tensor names (blk.48.nextn.hc_head_*) + MTP-layer compress ratio; weights unchanged. Loads in upstream llama.cpp b11386+ and agentionai/llama.cpp main 2026-10-05+ 22 days ago