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 / docs /assistants /README.md

EldanRing

Add verified NVFP4 and matching MTP assistant assets

398eb4a verified 7 days ago

preview code

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843 Bytes

E4B MTP assistant

Gemma-4-E4B-IT-Assistant-BF16.gguf is a BF16 GGUF conversion of Google's official google/gemma-4-E4B-it-assistant at revision 8d0031ea8c2109e2b1e86bb9368a4539b537f80a. Conversion changes serialization and performs no training.

File size: 171766688 bytes. SHA256: 4e3c9d335b248ced9bd3e0584547efca30dbf6cfd0debb810613f811a575162a. The matching Winnow launcher verifies the complete file before loading; other assistant files are not silently substituted.

The original upstream model card and Apache-2.0 license apply. See LICENSE-APACHE-2.0.txt, NOTICE.txt and provenance.json in this directory for attribution and exact conversion provenance.