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
- llama.cpp
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
Commit History
7 days ago
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Add decision-model tag to model card
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14 days ago
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Fix checksum and license links
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Publish approved Winnow card, usage and evidence
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15 days ago
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Clarify target downloads and optional MTP assistant metadata
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16 days ago
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Fix checksum and license file links in model card
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Publish Winnow runtime guidance, measured results and model cards
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Add verified NVFP4 and matching MTP assistant assets
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20 days ago
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Classify Winnow-E4B as image-text-to-text
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26 days ago
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Update E4B comparison figure title
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Add verified benchmark figures to model card
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Refocus Winnow-E4B model card on model and decision workflow
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Recommend Q8 profiles for 10 GB and 12 GB GPUs
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Package E4B vision projector and verified 64K profile
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Clarify BF16 validation scope
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Add evaluated step-438 BF16 GGUF
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Add E4B model card, evaluation, and quickstart
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Add Winnow-E4B Q8 model weights
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initial commit
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