Transformers
GGUF
sparse-attention
approximate-nearest-neighbors
faiss
qwen3
long-context
conversational
Instructions to use datasysdev/ann-sparseattention with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datasysdev/ann-sparseattention with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("datasysdev/ann-sparseattention", dtype="auto") - llama-cpp-python
How to use datasysdev/ann-sparseattention with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="datasysdev/ann-sparseattention", filename="gguf/Qwen3-4B-Instruct-2507-F16-ann-6layer-k128-v2.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use datasysdev/ann-sparseattention 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 datasysdev/ann-sparseattention:F16 # Run inference directly in the terminal: llama cli -hf datasysdev/ann-sparseattention:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf datasysdev/ann-sparseattention:F16 # Run inference directly in the terminal: llama cli -hf datasysdev/ann-sparseattention:F16
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 datasysdev/ann-sparseattention:F16 # Run inference directly in the terminal: ./llama-cli -hf datasysdev/ann-sparseattention:F16
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 datasysdev/ann-sparseattention:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf datasysdev/ann-sparseattention:F16
Use Docker
docker model run hf.co/datasysdev/ann-sparseattention:F16
- LM Studio
- Jan
- Ollama
How to use datasysdev/ann-sparseattention with Ollama:
ollama run hf.co/datasysdev/ann-sparseattention:F16
- Unsloth Studio
How to use datasysdev/ann-sparseattention 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 datasysdev/ann-sparseattention 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 datasysdev/ann-sparseattention to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for datasysdev/ann-sparseattention to start chatting
- Pi
How to use datasysdev/ann-sparseattention with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf datasysdev/ann-sparseattention:F16
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": "datasysdev/ann-sparseattention:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use datasysdev/ann-sparseattention with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf datasysdev/ann-sparseattention:F16
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 datasysdev/ann-sparseattention:F16
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use datasysdev/ann-sparseattention with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf datasysdev/ann-sparseattention:F16
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 "datasysdev/ann-sparseattention:F16" \ --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 datasysdev/ann-sparseattention with Docker Model Runner:
docker model run hf.co/datasysdev/ann-sparseattention:F16
- Lemonade
How to use datasysdev/ann-sparseattention with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull datasysdev/ann-sparseattention:F16
Run and chat with the model
lemonade run user.ann-sparseattention-F16
List all available models
lemonade list
Add related-work positioning table
Browse files
README.md
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The next run reserves `[0, 1, 2, 35]` and trains layers `3..34`.
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## Checkpoints
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Important checkpoint paths in this HF repo:
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The next run reserves `[0, 1, 2, 35]` and trains layers `3..34`.
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## Positioning against related methods
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The paper frames this method as closest in asymptotic shape to Reformer and
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closest in practical baseline behavior to Quest.
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| Method | Selection mechanism | Query-aware | Trained | Asymptotic | Exact softmax |
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| Full attention | all keys | n/a | n/a | O(N²) | yes |
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| Reformer | LSH hashing | yes | no | O(N log N) | over bucket |
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| Performer | random features | n/a | no | O(N) | no |
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| BigBird | window + random + global | mostly no | no | O(N) | over pattern |
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| Longformer | sliding window + global | mostly no | no | O(N) | over pattern |
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| NSA-style methods | block compression/selection | partial | partial | O(N²) proxy | yes |
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| Quest | min/max page heuristic | yes | no | O(N) | over pages |
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| This work | trained low-dim retrieval | yes | yes | O(N log N) | over retrieved set |
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This is a design-positioning table, not a claim of completed production
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superiority. The clean result proves the approach for the six-layer pilot; the
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active all32 reserved-layer run tests whether broad near-whole-model
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substitution can preserve that quality.
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## Checkpoints
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Important checkpoint paths in this HF repo:
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