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
- llama.cpp
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
Download docs/comparison-summary.json from EldanRing/Winnow-E2B: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/EldanRing/Winnow-E2B/resolve/main/docs/comparison-summary.json
- Command line
-
hf download hf://EldanRing/Winnow-E2B/docs/comparison-summary.json
-
curl -L -o comparison-summary.json https://huggingface.co/EldanRing/Winnow-E2B/resolve/main/docs/comparison-summary.json
3.58 kB
| { | |
| "title": "E2B / E4B direct and adaptive comparison", | |
| "date": "2026-10-06", | |
| "metric": "Verified-label accuracy for Jev/Kev; synthetic teacher-label agreement for Typed", | |
| "scope": "Previously exposed panels; same frozen cases; E2B integrated v3 and retained E4B comparison; model-specific policies and generation paths", | |
| "common_settings": { | |
| "target_weights": "Q8", | |
| "target_kv": "F16", | |
| "context": 8192, | |
| "mtp_draft_length": 4, | |
| "chat_slots": 1, | |
| "draft_cache_flags": "Q8_0 (retained launch flags)", | |
| "assistant_attention_kv": "F16 (shared target K/V tensors)" | |
| }, | |
| "e2b": { | |
| "gate": "raw maxP < 0.99", | |
| "direct_augmented_weights": [ | |
| 0.5, | |
| 0.5 | |
| ], | |
| "sampler": "backend temperature sampling", | |
| "batch": 1024, | |
| "ubatch": 1024, | |
| "deadline_seconds": 180, | |
| "adaptive_execution": "actual integrated v3 run", | |
| "source_commit": "ee6bd37d34ae35d2e69ebb0c4b0957a10c727357", | |
| "policy": "e2b-raw99-blend50-v3", | |
| "prompt": "native labels with safe input escaping and structured-state ownership" | |
| }, | |
| "e4b": { | |
| "policy": "e4b-calibrated75-g95-v1", | |
| "gate": "raw maxP < 0.95", | |
| "direct_augmented_weights": [ | |
| 0.25, | |
| 0.75 | |
| ], | |
| "sampler": "released sampler; no E2B backend-temperature flag", | |
| "batch": 2048, | |
| "ubatch": 1024, | |
| "deadline_seconds": 75, | |
| "adaptive_execution": "live selective client", | |
| "calibration_scope": "F16 KV outside measured release profile; temperatures held fixed" | |
| }, | |
| "receipt_sha256": { | |
| "e4b-e2b-comparison-analysis.json": "b19713c86fc58ab837137009bb8796a6fb82c717465f9c84cfc63cafe5f8d19f", | |
| "e4b-e2b-comparison-freeze.json": "6ffc48de7155b73d69659ee7757b122354813b0984b02712a84b89854c40eacd", | |
| "e2b-v3-analysis.json": "1cd872865228fb33adaf12dd48d3656b98fd6f1f499d8adf364e04de4f1ddb27", | |
| "e2b-v3-freeze.json": "eb163f63b0eb3a706adaaf18d20ebe3acaef4ddb0690005149eef2cbc750d2a6", | |
| "e2b-v3-completion.json": "6e8eb1e7f38bb725321b5bc24683ec8ccfb24b34a92d5de15e94f776d30e26b0" | |
| }, | |
| "panels": [ | |
| { | |
| "key": "jevbench-public", | |
| "title": "JevBench", | |
| "n": 231, | |
| "source_sha256": "bd304bc512d70bcbd9ff0b7d039d241b86aa0072ae5533c3c8d493c170aeb4d0", | |
| "E2B": { | |
| "direct": 175, | |
| "adaptive": 202, | |
| "direct_pct": "75.76", | |
| "adaptive_pct": "87.45", | |
| "change_pp": "+11.69" | |
| }, | |
| "E4B": { | |
| "direct": 183, | |
| "adaptive": 203, | |
| "direct_pct": "79.22", | |
| "adaptive_pct": "87.88", | |
| "change_pp": "+8.66" | |
| } | |
| }, | |
| { | |
| "key": "kev-v9-clean", | |
| "title": "Kev v9 clean", | |
| "n": 1046, | |
| "source_sha256": "4ca4b28a171d6130f00e078316e9cdfcdedd0933a1b5fb967517c2e20244ad90", | |
| "E2B": { | |
| "direct": 728, | |
| "adaptive": 851, | |
| "direct_pct": "69.60", | |
| "adaptive_pct": "81.36", | |
| "change_pp": "+11.76" | |
| }, | |
| "E4B": { | |
| "direct": 762, | |
| "adaptive": 842, | |
| "direct_pct": "72.85", | |
| "adaptive_pct": "80.50", | |
| "change_pp": "+7.65" | |
| } | |
| }, | |
| { | |
| "key": "typed-decisions", | |
| "title": "Typed", | |
| "n": 2000, | |
| "source_sha256": "143541319ddc4445ba67098c0af0480646e8124d682365d649b8e5947298cc0d", | |
| "E2B": { | |
| "direct": 1237, | |
| "adaptive": 1366, | |
| "direct_pct": "61.85", | |
| "adaptive_pct": "68.30", | |
| "change_pp": "+6.45" | |
| }, | |
| "E4B": { | |
| "direct": 1446, | |
| "adaptive": 1439, | |
| "direct_pct": "72.30", | |
| "adaptive_pct": "71.95", | |
| "change_pp": "\u22120.35" | |
| } | |
| } | |
| ] | |
| } | |