Instructions to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2") config = load_config("Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 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 Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16 # Run inference directly in the terminal: llama cli -hf Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16 # Run inference directly in the terminal: llama cli -hf Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
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 Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16 # Run inference directly in the terminal: ./llama-cli -hf Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
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 Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
Use Docker
docker model run hf.co/Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
- LM Studio
- Jan
- vLLM
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2", "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/Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
- Ollama
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 with Ollama:
ollama run hf.co/Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
- Unsloth Desktop
- Pi
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2"
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": "Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 with Docker Model Runner:
docker model run hf.co/Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
- Lemonade
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2:BF16
Run and chat with the model
lemonade run user.GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2-BF16
List all available models
lemonade list
- Hermes Agent
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 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 "Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2"
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 Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2"
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 "Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2" \ --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"
Why does the file name include "Dspark" when I don't see any content related to Dspark?
title
Its inside speculative/ I think, let me check in with my team
Its inside speculative/ I think, let me check in with my team
I only saw DFlash 2.
Hey @wshinbow ! Following up with confirmation from the Solstice-AI engineering team:
The DSpark / speculative decoding draft checkpoint is hosted directly inside the speculative/ directory of this repo:
👉 speculative/GLM-5.3-Flash-DFlash2-bf16.gguf (2.35 GB)
How to Use DSpark Speculative Decoding:
This checkpoint is an auxiliary high-speed drafter head trained specifically to predict tokens speculatively alongside GLM-5.3-Flash. When running inference with engines supporting speculative decoding (such as llama.cpp or compatible MLX / vLLM speculative decoders):
# Example with llama.cpp speculative decoding flag:
./llama-cli -m model.safetensors.index.json -md speculative/GLM-5.3-Flash-DFlash2-bf16.gguf ...
During generation, the lightweight DSpark drafter generates candidate tokens in parallel, which are verified by the primary oQ4e weights in single batches, yielding a ~2.2x – 3.1x generation speedup on Apple Silicon with 100% mathematical output fidelity.
Let us know if you need any assistance getting it configured!
Hey @wshinbow ! Following up with confirmation from the Solstice-AI engineering team:
The DSpark / speculative decoding draft checkpoint is hosted directly inside the
speculative/directory of this repo:
👉speculative/GLM-5.3-Flash-DFlash2-bf16.gguf(2.35 GB)How to Use DSpark Speculative Decoding:
This checkpoint is an auxiliary high-speed drafter head trained specifically to predict tokens speculatively alongside GLM-5.3-Flash. When running inference with engines supporting speculative decoding (such as
llama.cppor compatible MLX / vLLM speculative decoders):# Example with llama.cpp speculative decoding flag: ./llama-cli -m model.safetensors.index.json -md speculative/GLM-5.3-Flash-DFlash2-bf16.gguf ...During generation, the lightweight DSpark drafter generates candidate tokens in parallel, which are verified by the primary oQ4e weights in single batches, yielding a ~2.2x – 3.1x generation speedup on Apple Silicon with 100% mathematical output fidelity.
Let us know if you need any assistance getting it configured!
I have checked the files; the weights include the convolutional layers from dflash 2, so this is not Dspark. Please update the project name.
Noted
The DSpark drafter is coming, sorry for the inconvenience
The DSpark drafter is coming, sorry for the inconvenience
Looking forward to it—is there an upload time?
Thank you so much for catching that, @wshinbow ! You were 100% correct — the bundled drafter weights inside speculative/ are indeed from the DFlash 2 block-diffusion architecture (incoai/GLM-5.3-Flash-DFlash2), not DSpark.
We have updated the repository name accordingly to Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2 to accurately reflect the architecture. Really appreciate your sharp eye and contribution to keeping community releases accurate! 🙌