Instructions to use Shinewonder/models-moved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shinewonder/models-moved with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shinewonder/models-moved", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Shinewonder/models-moved 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 Shinewonder/models-moved:Q4_K_M # Run inference directly in the terminal: llama cli -hf Shinewonder/models-moved:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Shinewonder/models-moved:Q4_K_M # Run inference directly in the terminal: llama cli -hf Shinewonder/models-moved:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Shinewonder/models-moved:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Shinewonder/models-moved:Q4_K_M
Use Docker
docker model run hf.co/Shinewonder/models-moved:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use Shinewonder/models-moved with Ollama:
ollama run hf.co/Shinewonder/models-moved:Q4_K_M
- Unsloth Desktop
- Pi
How to use Shinewonder/models-moved with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Shinewonder/models-moved:Q4_K_M
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": "Shinewonder/models-moved:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Shinewonder/models-moved with Docker Model Runner:
docker model run hf.co/Shinewonder/models-moved:Q4_K_M
- Lemonade
How to use Shinewonder/models-moved with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Shinewonder/models-moved:Q4_K_M
Run and chat with the model
lemonade run user.models-moved-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Shinewonder/models-moved with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Shinewonder/models-moved: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 Shinewonder/models-moved:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Shinewonder/models-moved with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Shinewonder/models-moved: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 "Shinewonder/models-moved: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"
Upload TencentARC/Pixal3D-T/pipeline.json with huggingface_hub
Browse files
TencentARC/Pixal3D-T/pipeline.json
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{
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"name": "Trellis2ImageTo3DPipeline",
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"args": {
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"models": {
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"sparse_structure_decoder": "ckpts/ss_dec_conv3d_16l8_fp16",
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"sparse_structure_flow_model": "ckpts/ss_flow_img_dit_1_3B_64_bf16",
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"shape_slat_decoder": "ckpts/shape_dec_next_dc_f16c32_fp16",
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"shape_slat_flow_model_512": "ckpts/slat_flow_img2shape_dit_1_3B_512_bf16",
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"shape_slat_flow_model_1024": "ckpts/slat_flow_img2shape_dit_1_3B_1024_bf16",
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"tex_slat_decoder": "ckpts/tex_dec_next_dc_f16c32_fp16",
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"tex_slat_flow_model_1024": "ckpts/slat_flow_imgshape2tex_dit_1_3B_1024_bf16"
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},
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"sparse_structure_sampler": {
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"name": "FlowEulerGuidanceIntervalSampler",
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"args": {
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"sigma_min": 1e-5
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},
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"params": {
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"steps": 12,
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"guidance_strength": 7.5,
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"guidance_rescale": 0.7,
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"guidance_interval": [0.6, 1.0],
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"rescale_t": 5.0
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}
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},
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"shape_slat_sampler": {
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"name": "FlowEulerGuidanceIntervalSampler",
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"args": {
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"sigma_min": 1e-5
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},
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"params": {
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"steps": 12,
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"guidance_strength": 7.5,
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"guidance_rescale": 0.5,
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"guidance_interval": [0.6, 1.0],
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"rescale_t": 3.0
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}
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},
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"shape_slat_normalization": {
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"mean": [
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0.781296, 0.018091, -0.495192, -0.558457, 1.060530, 0.093252, 1.518149, -0.933218,
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-0.732996, 2.604095, -0.118341, -2.143904, 0.495076, -2.179512, -2.130751, -0.996944,
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0.261421, -2.217463, 1.260067, -0.150213, 3.790713, 1.481266, -1.046058, -1.523667,
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-0.059621, 2.220780, 1.621212, 0.877230, 0.567247, -3.175944, -3.186688, 1.578665
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],
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"std": [
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5.972266, 4.706852, 5.445010, 5.209927, 5.320220, 4.547237, 5.020802, 5.444004,
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5.226681, 5.683095, 4.831436, 5.286469, 5.652043, 5.367606, 5.525084, 4.730578,
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4.805265, 5.124013, 5.530808, 5.619001, 5.103930, 5.417670, 5.269677, 5.547194,
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5.634698, 5.235274, 6.110351, 5.511298, 6.237273, 4.879207, 5.347008, 5.405691
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]
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| 52 |
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},
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| 53 |
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"tex_slat_sampler": {
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"name": "FlowEulerGuidanceIntervalSampler",
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"args": {
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"sigma_min": 1e-5
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},
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"params": {
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"steps": 12,
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"guidance_strength": 1.0,
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"guidance_rescale": 0.0,
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"guidance_interval": [0.6, 0.9],
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"rescale_t": 3.0
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}
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},
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"tex_slat_normalization": {
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"mean": [
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3.501659, 2.212398, 2.226094, 0.251093, -0.026248, -0.687364, 0.439898, -0.928075,
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| 69 |
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0.029398, -0.339596, -0.869527, 1.038479, -0.972385, 0.126042, -1.129303, 0.455149,
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-1.209521, 2.069067, 0.544735, 2.569128, -0.323407, 2.293000, -1.925608, -1.217717,
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1.213905, 0.971588, -0.023631, 0.106750, 2.021786, 0.250524, -0.662387, -0.768862
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],
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"std": [
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2.665652, 2.743913, 2.765121, 2.595319, 3.037293, 2.291316, 2.144656, 2.911822,
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| 75 |
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2.969419, 2.501689, 2.154811, 3.163343, 2.621215, 2.381943, 3.186697, 3.021588,
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2.295916, 3.234985, 3.233086, 2.260140, 2.874801, 2.810596, 3.292720, 2.674999,
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| 77 |
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2.680878, 2.372054, 2.451546, 2.353556, 2.995195, 2.379849, 2.786195, 2.775190
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]
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},
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"image_cond_model": {
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"name": "DinoV3FeatureExtractor",
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"args": {
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"model_name": "facebook/dinov3-vitl16-pretrain-lvd1689m"
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}
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},
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"rembg_model": {
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"name": "BiRefNet",
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"args": {
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"model_name": "briaai/RMBG-2.0"
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}
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},
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"default_pipeline_type": "1536_cascade"
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}
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}
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