Instructions to use Baekpica/Motif-3-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 Baekpica/Motif-3-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 Baekpica/Motif-3-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Baekpica/Motif-3-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Baekpica/Motif-3-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Baekpica/Motif-3-GGUF:Q8_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 Baekpica/Motif-3-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Baekpica/Motif-3-GGUF:Q8_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 Baekpica/Motif-3-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Baekpica/Motif-3-GGUF:Q8_0
Use Docker
docker model run hf.co/Baekpica/Motif-3-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use Baekpica/Motif-3-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Baekpica/Motif-3-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": "Baekpica/Motif-3-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Baekpica/Motif-3-GGUF:Q8_0
- Ollama
How to use Baekpica/Motif-3-GGUF with Ollama:
ollama run hf.co/Baekpica/Motif-3-GGUF:Q8_0
- Unsloth Studio
How to use Baekpica/Motif-3-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 Baekpica/Motif-3-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 Baekpica/Motif-3-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Baekpica/Motif-3-GGUF to start chatting
- Pi
How to use Baekpica/Motif-3-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Baekpica/Motif-3-GGUF:Q8_0
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": "Baekpica/Motif-3-GGUF:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use Baekpica/Motif-3-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Baekpica/Motif-3-GGUF:Q8_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 "Baekpica/Motif-3-GGUF:Q8_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"
- Docker Model Runner
How to use Baekpica/Motif-3-GGUF with Docker Model Runner:
docker model run hf.co/Baekpica/Motif-3-GGUF:Q8_0
- Lemonade
How to use Baekpica/Motif-3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Baekpica/Motif-3-GGUF:Q8_0
Run and chat with the model
lemonade run user.Motif-3-GGUF-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use Baekpica/Motif-3-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 Baekpica/Motif-3-GGUF:Q8_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 Baekpica/Motif-3-GGUF:Q8_0
Run Hermes
hermes
- Atomic Chat
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 Baekpica/Motif-3-GGUF:Q8_0Run Hermes
hermesMotif 3 โ Q8_0 GGUF
Full-topology Q8_0 reference conversion of
Motif-Technologies/Motif-3.
This is an independent conversion, not an official Motif Technologies release.
The conversion retains all 53 transformer layers, including the first two dense layers and all 51 sparse layers; all 384 routed experts per sparse layer (top-8 routing); the shared expert; Grouped Differential Latent Attention (GDLA); Expert-Specific PolyNorm; modified mHC; and the one-layer MTP head. Nothing is pruned, merged, expert-dropped, or layer-dropped.
Artifact
| Variant | Split | Exact size | Purpose |
|---|---|---|---|
| Q8_0 reference | 11 shards | 334,810,734,464 bytes (311.817 GiB) | imatrix calibration and full-model correctness reference |
Start with Motif-3-Q8_0-00001-of-00011.gguf; split-aware runtimes discover
the remaining shards automatically. Exact hashes are in Q8_0-SHA256SUMS.
The weight files are fixed at Hub revision
5c266c95bf8c8d822d50e5e1cce9d108eaadb2af. Later model-card commits do
not change shard bytes or hashes.
hf download Baekpica/Motif-3-GGUF \
--revision 5c266c95bf8c8d822d50e5e1cce9d108eaadb2af \
--include 'Motif-3-Q8_0-*.gguf' \
--include Q8_0-SHA256SUMS \
--local-dir ./Motif-3-Q8_0
This reference follows the control-path protection policy used by the mixed release. Its 2,287 GGUF tensors comprise:
| Tensor type | Count | Role |
|---|---|---|
Q8_0 |
696 | embeddings, LM head, GDLA/dense/shared/MTP projections, all routed expert gate/up/down matrices |
BF16 |
318 | small mHC projection matrices |
F32 |
1,273 | routers, norms, PolyNorm coefficients/biases, and mHC controls/scalars |
The protected BF16/F32 tensors are intentional. โQ8_0 referenceโ means every large quantizable weight matrix, including every routed expert matrix, is Q8_0; decision-sensitive controls remain at their locked higher precision.
Provenance
| Source model | Motif-Technologies/Motif-3 |
| Exact source revision | ccceb1a5fd7b5eb32e47841216b3caf5666c07bc |
| Source parameters | 314,841,775,750 |
| Source tensors | 2,236 |
| GGUF tensors | 2,287 (51 fused gate/up tensors are losslessly separated) |
| GGUF architecture | motif3 |
| Native context metadata | 262,144 tokens |
| Fixed Q8-weight revision | 5c266c95bf8c8d822d50e5e1cce9d108eaadb2af |
| Official implementation oracle | MotifTechnologies/vllm@4cd9eb4129883565e69d508038d783d59ee01867 |
| Conversion base | ggml-org/llama.cpp@1d2869c6e54d5003f3927a79efbca0fefa034a6d |
| Native runtime | Baekpica/ds4:feature/motif-3-model-loader@d878ea1 |
| Public reproduction | Baekpica/motif-3-mixed-ds4 |
| Private Spark handoff | Expensive state is preserved in hf://buckets/Baekpica/motif-3-spark-handoff |
Only the official final Motif-3 checkpoint above was used. Motif-3-Beta was not used as a source, calibration input, implementation oracle, or fallback.
Validation status
The completed 11-shard artifact passed a strict source-map verification:
- all 2,287 expected GGUF tensors are present exactly once;
- every shape, type, byte length, and split index/count matches the locked map;
- all 53 layers are present, including routed gate/up/down tensors for sparse layers 2โ52 and the full MTP block;
general.source.revisionis the pinned official revision above;- the aggregate verified tensor payload is 334,801,074,712 bytes;
- no structural-template marker remains in the release files.
A separate source-row sanity pass dequantized 57 representative rows spanning
the embedding, LM head, GDLA, dense/shared paths, routed gate/up/down matrices
at sparse layers 2/26/52, protected controls, and MTP. It also samples both
halves of the fused source gate/up tensors across experts 0/173/383. F32 and
BF16 protected rows were source-exact; Q8_0 cosine was at least
0.9999740124, with maximum sampled relative RMSE 0.007201264.
The machine-readable report and complete per-shard SHA-256 list are included
as Q8_0-VERIFY.json, Q8_0-SAMPLE-VERIFY.json, and
Q8_0-SHA256SUMS.
The 262,144-token value is source architecture metadata. This Q8 repository
does not by itself claim completed single-DGX-Spark 256K serving. That release
gate belongs to the native motif3 ds4 runtime and requires measured resident
weights, latent KV, workspace/server memory, prefill, decode, and semantic
checks on GB10.
Runtime compatibility
Motif 3 is not a Llama-family graph. A runtime must implement its 384E sigmoid
router and route normalization, shared expert, Expert-Specific PolyNorm,
GDLA/differential heads and output gate, modified mHC, interleaved SWA/full
attention with YaRN, latent KV semantics, and MTP. Do not assume that a stock
GGUF runtime recognizes motif3 merely because it can parse the container.
The H200 development branch is publicly available as
Baekpica/ds4:feature/motif-3-model-loader
at exact implementation commit
d878ea1a1d67bc0f0bd60e20e75b4a011aa2d8d9. The private Spark handoff also
preserves an offline source snapshot and commit metadata.
Intended use in the mixed-quant pipeline
This artifact is produced first and frozen as the full-model Q8_0 reference.
The next stage collects an activation importance matrix from the pinned
calibration corpus, after which routed gate/up weights are quantized to
IQ2_XXS with that imatrix and routed down weights to Q2_K. The resulting
MQ87โ88 artifact is published separately at
Baekpica/Motif-3-Mixed-Quant-GGUF.
License and attribution
The source model identifies its license as MIT. See the official
Motif-Technologies/Motif-3 model card
for the architecture, intended-use, evaluation, citation, and license context.
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Base model
Motif-Technologies/Motif-3-Base
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp# Start a local OpenAI-compatible server: llama serve -hf Baekpica/Motif-3-GGUF:Q8_0