Instructions to use EldanRing/Winnow-E4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use EldanRing/Winnow-E4B 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 EldanRing/Winnow-E4B:BF16
# Run inference directly in the terminal:
llama cli -hf EldanRing/Winnow-E4B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EldanRing/Winnow-E4B:BF16
# Run inference directly in the terminal:
llama cli -hf EldanRing/Winnow-E4B:BF16

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 EldanRing/Winnow-E4B:BF16
# Run inference directly in the terminal:
./llama-cli -hf EldanRing/Winnow-E4B:BF16

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 EldanRing/Winnow-E4B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EldanRing/Winnow-E4B:BF16

Use Docker

docker model run hf.co/EldanRing/Winnow-E4B:BF16

LM Studio
Jan

vLLM

How to use EldanRing/Winnow-E4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EldanRing/Winnow-E4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EldanRing/Winnow-E4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EldanRing/Winnow-E4B:BF16

Ollama
How to use EldanRing/Winnow-E4B with Ollama:
```
ollama run hf.co/EldanRing/Winnow-E4B:BF16
```
Unsloth Desktop
Docker Model Runner
How to use EldanRing/Winnow-E4B with Docker Model Runner:
```
docker model run hf.co/EldanRing/Winnow-E4B:BF16
```

Lemonade

How to use EldanRing/Winnow-E4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EldanRing/Winnow-E4B:BF16

Run and chat with the model

lemonade run user.Winnow-E4B-BF16

List all available models

lemonade list

Atomic Chat

Winnow-E4B / NOTICE

EldanRing

Add E4B model card, evaluation, and quickstart

7bd6326 verified 17 days ago

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883 Bytes

	Winnow-E4B

	Base model: Gemma 4 E4B IT by Google DeepMind
	Source: https://huggingface.co/google/gemma-4-E4B-it
	Source revision: ee0ef6023621cff504d758262d4e04895a5af4a2
	Upstream license: Apache License, Version 2.0
	License reference: https://ai.google.dev/gemma/docs/gemma_4_license

	Winnow modifications are developed by EldanRing. The released Q8_0 GGUF
	modifies the base through task-specific LoRA fine-tuning, adapter merging and
	quantization. Its checksum and provenance are recorded in SHA256SUMS and
	release-manifest.json. The private training dataset and native checkpoint are
	not included in this model repository.

	The separate Winnow inference repository builds on llama.cpp by Georgi
	Gerganov and contributors under the MIT license. No llama.cpp source or binary
	is included in this model repository. Winnow is an independent project; no
	upstream endorsement is implied.