Instructions to use seamon67/Zerank-1-Small-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use seamon67/Zerank-1-Small-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 seamon67/Zerank-1-Small-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf seamon67/Zerank-1-Small-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf seamon67/Zerank-1-Small-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf seamon67/Zerank-1-Small-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 seamon67/Zerank-1-Small-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf seamon67/Zerank-1-Small-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 seamon67/Zerank-1-Small-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf seamon67/Zerank-1-Small-GGUF:Q8_0

Use Docker

docker model run hf.co/seamon67/Zerank-1-Small-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use seamon67/Zerank-1-Small-GGUF with Ollama:
```
ollama run hf.co/seamon67/Zerank-1-Small-GGUF:Q8_0
```

Unsloth Studio

How to use seamon67/Zerank-1-Small-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 seamon67/Zerank-1-Small-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 seamon67/Zerank-1-Small-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for seamon67/Zerank-1-Small-GGUF to start chatting

How to use seamon67/Zerank-1-Small-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf seamon67/Zerank-1-Small-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": "seamon67/Zerank-1-Small-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use seamon67/Zerank-1-Small-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf seamon67/Zerank-1-Small-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 "seamon67/Zerank-1-Small-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 seamon67/Zerank-1-Small-GGUF with Docker Model Runner:
```
docker model run hf.co/seamon67/Zerank-1-Small-GGUF:Q8_0
```

Lemonade

How to use seamon67/Zerank-1-Small-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull seamon67/Zerank-1-Small-GGUF:Q8_0

Run and chat with the model

lemonade run user.Zerank-1-Small-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use seamon67/Zerank-1-Small-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 seamon67/Zerank-1-Small-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 seamon67/Zerank-1-Small-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

About

This model was converted to GGUF format from zeroentropy/zerank-1-small-reranker using a modified version of llama.cpp (release b10269). Refer to the original model card for more details on the model.

This is a working GGUF.

Most community GGUFs of Zerank-1-Small-Reranker produce garbage scores because of 4 issues:

Zerank 1 Small ships modeling_zeranker.py instead of 1_LogitScore directory.
Wrong Token Extracted -> yes (9693) vs the correct Yes (9454). Also, no false token on Zerank 1 Small.
Wrong Scoring Formula -> Zerank 1 Small needs sigmoid(yes_logit / 5).
Slightly different chat template.

This GGUF fixes all 4 problems.

Original Model Card

Releasing zeroentropy/zerank-1-small

In search enginers, rerankers are crucial for improving the accuracy of your retrieval system.

This 1.7B reranker is the smaller version of our flagship model zeroentropy/zerank-1. Though the model is over 2x smaller, it maintains nearly the same standard of performance, continuing to outperform other popular rerankers, and displaying massive accuracy gains over traditional vector search.

We release this model under the open-source Apache License 2.0, in order to support the open-source community and push the frontier of what's possible with open-source models.

Model Details

Property	Value
Parameters	1.7B
Context Length	32,768 tokens (32k)
Base Model	Qwen/Qwen3-4B
License	Apache-2.0

How to Use

from sentence_transformers import CrossEncoder

model = CrossEncoder("zeroentropy/zerank-1-small", trust_remote_code=True)

query_documents = [
    ("What is 2+2?", "4"),
    ("What is 2+2?", "The answer is definitely 1 million"),
]

scores = model.predict(query_documents)
print(scores)

The model can also be inferenced using ZeroEntropy's /models/rerank endpoint.

Evaluations

NDCG@10 scores between zerank-1-small and competing closed-source proprietary rerankers. Since we are evaluating rerankers, OpenAI's text-embedding-3-small is used as an initial retriever for the Top 100 candidate documents.

Task	Embedding	cohere-rerank-v3.5	Salesforce/Llama-rank-v1	zerank-1-small	zerank-1
Code	0.678	0.724	0.694	0.730	0.754
Conversational	0.250	0.571	0.484	0.556	0.596
Finance	0.839	0.824	0.828	0.861	0.894
Legal	0.703	0.804	0.767	0.817	0.821
Medical	0.619	0.750	0.719	0.773	0.796
STEM	0.401	0.510	0.595	0.680	0.694