Instructions to use EldanRing/Winnow-E2B 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-E2B 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-E2B:BF16
# Run inference directly in the terminal:
llama cli -hf EldanRing/Winnow-E2B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EldanRing/Winnow-E2B:BF16
# Run inference directly in the terminal:
llama cli -hf EldanRing/Winnow-E2B: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-E2B:BF16
# Run inference directly in the terminal:
./llama-cli -hf EldanRing/Winnow-E2B: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-E2B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EldanRing/Winnow-E2B:BF16

Use Docker

docker model run hf.co/EldanRing/Winnow-E2B:BF16

LM Studio
Jan

vLLM

How to use EldanRing/Winnow-E2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EldanRing/Winnow-E2B"
# 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-E2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EldanRing/Winnow-E2B:BF16

Ollama
How to use EldanRing/Winnow-E2B with Ollama:
```
ollama run hf.co/EldanRing/Winnow-E2B:BF16
```
Unsloth Desktop
Docker Model Runner
How to use EldanRing/Winnow-E2B with Docker Model Runner:
```
docker model run hf.co/EldanRing/Winnow-E2B:BF16
```

Lemonade

How to use EldanRing/Winnow-E2B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EldanRing/Winnow-E2B:BF16

Run and chat with the model

lemonade run user.Winnow-E2B-BF16

List all available models

lemonade list

Atomic Chat

Winnow-E2B

File size: 843 Bytes

f0f9931

# E2B MTP assistant

`Gemma-4-E2B-IT-Assistant-BF16.gguf` is a BF16 GGUF conversion of Google's official [google/gemma-4-E2B-it-assistant](https://huggingface.co/google/gemma-4-E2B-it-assistant) at revision `2d874ef7d29f9a30599a1e4b3c1cbc9595f005df`. Conversion changes serialization and performs no training.

File size: 170194016 bytes. SHA256: `0772dcc50761a47a2da87151d3105ad2b82994d9dd1dae3ad3c5c0cd26086b16`. The matching Winnow launcher verifies the complete file before loading; other assistant files are not silently substituted.

The original [upstream model card](https://huggingface.co/google/gemma-4-E2B-it-assistant/blob/2d874ef7d29f9a30599a1e4b3c1cbc9595f005df/README.md) 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.