Instructions to use datasysdev/ann-sparseattention with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

llama.cpp

How to use datasysdev/ann-sparseattention with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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 new

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-server -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-server -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

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

datasysdev commited on 7 days ago

Commit

c740ca3

verified ·

1 Parent(s): 92ec570

Upload search_step_2000.k_sweep.json with huggingface_hub

Browse files

Files changed (1) hide show

search_step_2000.k_sweep.json +90 -0

search_step_2000.k_sweep.json ADDED Viewed

	@@ -0,0 +1,90 @@

+{
+  "ppl_full": 9.958177526791891,
+  "by_K": {
+    "16": {
+      "recall_avg": 0.2488617408769063,
+      "recall_per_layer": {
+        "4": 0.06929636437908497,
+        "8": 0.209304789624183,
+        "12": 0.2291079452614379,
+        "16": 0.31758450776143793,
+        "20": 0.3559002246732026,
+        "24": 0.3119766135620915
+      },
+      "ppl_ann": 10.705916802088419,
+      "ppl_gap_relative": 0.0750879639657738
+    },
+    "32": {
+      "recall_avg": 0.22758947123797024,
+      "recall_per_layer": {
+        "4": 0.08549663713910761,
+        "8": 0.1827639281906168,
+        "12": 0.20015622436843833,
+        "16": 0.29272973568733596,
+        "20": 0.31894826012959315,
+        "24": 0.28544204191272965
+      },
+      "ppl_ann": 10.40695869922638,
+      "ppl_gap_relative": 0.04506659689758181
+    },
+    "64": {
+      "recall_avg": 0.2313686687059083,
+      "recall_per_layer": {
+        "4": 0.109822914083168,
+        "8": 0.18851337735615079,
+        "12": 0.2026925869088955,
+        "16": 0.29454920531580686,
+        "20": 0.3052448898396164,
+        "24": 0.28738903873181215
+      },
+      "ppl_ann": 10.19960351785024,
+      "ppl_gap_relative": 0.02424399348262342
+    },
+    "128": {
+      "recall_avg": 0.2596596885325661,
+      "recall_per_layer": {
+        "4": 0.15761951733660953,
+        "8": 0.22230808709257394,
+        "12": 0.23406030798471103,
+        "16": 0.3191429876512097,
+        "20": 0.31382029543640794,
+        "24": 0.31100693569388443
+      },
+      "ppl_ann": 10.039695183436075,
+      "ppl_gap_relative": 0.008186001547458431
+    },
+    "256": {
+      "recall_avg": 0.3158585866292318,
+      "recall_per_layer": {
+        "4": 0.23482767740885416,
+        "8": 0.28606397840711806,
+        "12": 0.2944536844889323,
+        "16": 0.36897023518880206,
+        "20": 0.35041291978624134,
+        "24": 0.3604230244954427
+      },
+      "ppl_ann": 9.879923025767008,
+      "ppl_gap_relative": -0.0078583155215243
+    },
+    "512": {
+      "recall_avg": 0.4077308518545968,
+      "recall_per_layer": {
+        "4": 0.34663236708868117,
+        "8": 0.3869971320742652,
+        "12": 0.3905042466663179,
+        "16": 0.45224675678071524,
+        "20": 0.42589560009184335,
+        "24": 0.444109008425758
+      },
+      "ppl_ann": 9.670466581980387,
+      "ppl_gap_relative": -0.028891927668233962
+    }
+  },
+  "model": "Qwen/Qwen3-4B-Instruct-2507",
+  "checkpoint": "search_step_2000.pt",
+  "trained_layers": [4, 8, 12, 16, 20, 24],
+  "d_search": 64,
+  "seq_len": 4096,
+  "num_eval_batches": 12,
+  "eval_dataset": "Salesforce/wikitext (wikitext-103-raw-v1, validation split)"
+}