Instructions to use seamon67/Zembed-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/Zembed-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/Zembed-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf seamon67/Zembed-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/Zembed-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf seamon67/Zembed-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/Zembed-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf seamon67/Zembed-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/Zembed-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf seamon67/Zembed-GGUF:Q8_0

Use Docker

docker model run hf.co/seamon67/Zembed-GGUF:Q8_0

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

Unsloth Studio

How to use seamon67/Zembed-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/Zembed-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/Zembed-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/Zembed-GGUF to start chatting

How to use seamon67/Zembed-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/Zembed-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/Zembed-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use seamon67/Zembed-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/Zembed-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/Zembed-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/Zembed-GGUF with Docker Model Runner:
```
docker model run hf.co/seamon67/Zembed-GGUF:Q8_0
```

Lemonade

How to use seamon67/Zembed-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Zembed-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use seamon67/Zembed-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/Zembed-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/Zembed-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

Zembed-GGUF / README.md

seamon67

Upload Zembed GGUF

4b02c9a 3 days ago

preview code

Raw

History Blame Contribute Delete

5.31 kB

	---
	license: apache-2.0
	language:
	- en
	- multilingual
	base_model: zeroentropy/zembed-1-embedding
	base_model_relation: quantized
	pipeline_tag: feature-extraction
	tags:
	- gguf
	- finance
	- legal
	- healthcare
	- code
	- stem
	- medical
	- multilingual
	library_name: llama.cpp
	model_max_length: 32768
	---

	# About

	This model was converted to GGUF format from zeroentropy/zerank-2 using llama.cpp (release b10269). Refer to the [original model card](https://huggingface.co/zeroentropy/zembed-1-embedding) for more details on the model.

	# Original Model Card

	<img src="https://i.imgur.com/oxvhvQu.png"/>

	# Releasing zeroentropy/zembed-1

	In retrieval systems, [embedding models determine the quality of your search](https://www.zeroentropy.dev/articles/how-to-overcome-poor-search-results-with-the-right-embedding-solution).

	However, SOTA embedding models are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA 4B open-weight multilingual embedding model that outperforms every competitor we benchmarked, and we're launching it here on HuggingFace.

	This model [outperforms](https://huggingface.co/zeroentropy/zembed-1#evaluations) `OpenAI text-embedding-large`, `Cohere Embed v4`, `gemini-embedding-001`, and `voyage-4-nano` across finance, healthcare, legal, conversational, manufacturing, code, and STEM.

	zembed-1 is distilled directly from our SOTA reranker [zerank-2](https://huggingface.co/zeroentropy/zerank-2) using our [zELO methodology](https://arxiv.org/abs/2509.12541), which models relevance scores as adjusted [Elo ratings](https://en.wikipedia.org/wiki/Elo_rating_system). Standard contrastive training on binary labels can't match this signal. See [our blog post](https://www.zeroentropy.dev/articles/introducing-zembed-1-the-worlds-best-multilingual-text-embedding-model) for details.

	The model supports flexible dimension projections (2560, 1280, 640, 320, 160, 80, 40) and quantization down to binary, compressing a full 8 KB vector to under 128 bytes with a controlled accuracy trade-off. See our Technical Report (Coming soon!) for details on the projection method. zembed-1 is multilingual from the ground up, with over half the training data in non-English languages.

	This model is released under the [Apache License 2.0](./LICENSE).

	## Model Details

	\| Property \| Value \|
	\|---\|---\|
	\| Parameters \| 4B \|
	\| Context Length \| 32,768 tokens (32k) \|
	\| Base Model \| Qwen/Qwen3-4B \|
	\| Embedding Dimensions \| 2560, 1280, 640, 320, 160, 80, 40 \|
	\| License \| Apache-2.0 \|

	## How to Use

	```python
	from sentence_transformers import SentenceTransformer

	# Initialize model
	model = SentenceTransformer(
	"zeroentropy/zembed-1",
	trust_remote_code=True,
	model_kwargs={"torch_dtype": "bfloat16"},
	)

	# Define query and documents
	query = "What is backpropagation?"
	documents = [
	"Backpropagation is a fundamental algorithm for training neural networks by computing gradients.",
	"Gradient descent is used to optimize model parameters during the training process.",
	"Neural network training relies on efficient computation of derivatives through backpropagation.",
	]

	# Encode query and documents (uses task-specific prompts automatically)
	query_embeddings = model.encode_query(query)
	document_embeddings = model.encode_document(documents)
	# (2560,) (3, 2560)

	# Compute cosine similarities
	similarities = model.similarity(query_embeddings, document_embeddings)
	# tensor([[0.7525, 0.5670, 0.6835]])
	```

	The model can also be used through ZeroEntropy's [/models/embed](https://docs.zeroentropy.dev/api-reference/models/embed) endpoint.

	## Evaluations

	NDCG@10 scores between `zembed-1` and competing embedding models, averaged across public and private benchmarks per domain. Full per-benchmark breakdown [here](https://docs.google.com/spreadsheets/d/1qFXGZLMg6-O5tVLIJS3tpf5QNJxCHiiQtj35dZub4vY/edit?gid=0#gid=0).

	\| Domain \| ZeroEntropy zembed-1 \| voyage-4-nano \| Qwen3 4B \| Cohere Embed v4 \| gemini-embed-001 \| jina-v5-small \| OpenAI Large \| bge-m3 \|
	\|------------------\|----------------------\|---------------\|----------\|-----------------\|-------------------\|---------------\|--------------\|--------\|
	\| Finance \| 0.4476 \| 0.4227 \| 0.3715 \| 0.3670 \| 0.3291 \| 0.3576 \| 0.3291 \| 0.3085 \|
	\| Healthcare \| 0.6260 \| 0.5356 \| 0.5134 \| 0.4750 \| 0.5008 \| 0.5132 \| 0.5315 \| 0.3620 \|
	\| Legal \| 0.6723 \| 0.5957 \| 0.5858 \| 0.5894 \| 0.6069 \| 0.5716 \| 0.5099 \| 0.5207 \|
	\| Conversational \| 0.5385 \| 0.4045 \| 0.4034 \| 0.4244 \| 0.4247 \| 0.4430 \| 0.3988 \| 0.3296 \|
	\| Manufacturing \| 0.5556 \| 0.4857 \| 0.4932 \| 0.4919 \| 0.4664 \| 0.4725 \| 0.4736 \| 0.3736 \|
	\| Web Search \| 0.6165 \| 0.5977 \| 0.6914 \| 0.7242 \| 0.5881 \| 0.6772 \| 0.6750 \| 0.6311 \|
	\| Code \| 0.6452 \| 0.6415 \| 0.6379 \| 0.6277 \| 0.6305 \| 0.6354 \| 0.6155 \| 0.5584 \|
	\| STEM & Math \| 0.5283 \| 0.5012 \| 0.5219 \| 0.4698 \| 0.4840 \| 0.3780 \| 0.3905 \| 0.3399 \|
	\| Enterprise \| 0.3750 \| 0.3600 \| 0.2935 \| 0.2915 \| 0.3224 \| 0.3012 \| 0.3307 \| 0.2213 \|
	\| Average \| 0.5561 \| 0.5050 \| 0.5013 \| 0.4957 \| 0.4837 \| 0.4833 \| 0.4727 \| 0.4050 \|

	<img src="assets/zembed_eval_chart.png" alt="Bar chart comparing zembed-1 NDCG@10 scores against competing embedding models across domains" width="1000"/>

	## License

	This model is licensed under the [Apache License 2.0](./LICENSE).