Instructions to use lthn/lemmy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lthn/lemmy with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lthn/lemmy", filename="lemmy-bf16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use lthn/lemmy with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf lthn/lemmy:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lthn/lemmy:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf lthn/lemmy:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lthn/lemmy:Q4_K_M
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 lthn/lemmy:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf lthn/lemmy:Q4_K_M
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 lthn/lemmy:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lthn/lemmy:Q4_K_M
Use Docker
docker model run hf.co/lthn/lemmy:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use lthn/lemmy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lthn/lemmy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lthn/lemmy", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/lthn/lemmy:Q4_K_M
- Ollama
How to use lthn/lemmy with Ollama:
ollama run hf.co/lthn/lemmy:Q4_K_M
- Unsloth Studio
How to use lthn/lemmy with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lthn/lemmy to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lthn/lemmy to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lthn/lemmy to start chatting
- Pi
How to use lthn/lemmy with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf lthn/lemmy:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "lthn/lemmy:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use lthn/lemmy with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf lthn/lemmy:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default lthn/lemmy:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use lthn/lemmy with Docker Model Runner:
docker model run hf.co/lthn/lemmy:Q4_K_M
- Lemonade
How to use lthn/lemmy with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lthn/lemmy:Q4_K_M
Run and chat with the model
lemonade run user.lemmy-Q4_K_M
List all available models
lemonade list
eval: first 8-PAC paired run — strong LEK delta signal
Browse filesFirst paired benchmark for lemmy (26B A4B MoE) vs gemma-4-26b-a4b-it-4bit
on MMLU-Pro. n=4 r=8, same methodology as lemer/lemma.
Results:
base: 40.62% per-round, 25% majority (1/4)
lek: 71.88% per-round, 75% majority (3/4)
delta: +31.26pp per-round, +50pp majority
n=4 is well below the noise floor for a 32-domain benchmark — this is a
directional signal, not a statistical claim. But the delta direction and
magnitude on the biggest MoE in the family are worth capturing in the
canon for when larger runs contribute.
Canonical storage is .eval_results/mmlu_pro.{parquet,yaml,md} — same
layout as lemer and lemma.
Co-Authored-By: Virgil <virgil@lethean.io>
- .eval_results/mmlu_pro.md +21 -0
- .eval_results/mmlu_pro.parquet +3 -0
- .eval_results/mmlu_pro.yaml +24 -0
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# TIGER-Lab/MMLU-Pro / mmlu_pro — 8-PAC Canon
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Merged from 1 run(s) across 1 machine(s). Total rows: **64**.
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## Machines
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- `studio`: 64 rows
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## Scores
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| Side | Model | Samples | Questions | Rounds | Per-round acc | Majority acc |
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|---|---|---|---|---|---|---|
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| `base` | `mlx-community/gemma-4-26b-a4b-it-4bit` | 32 | 4 | 8 | 40.62% | 25.00% (1/4) |
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| `lek` | `lthn/lemmy` | 32 | 4 | 8 | 71.88% | 75.00% (3/4) |
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## LEK delta
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- per-round: **+31.26pp**
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- majority-vote: **+50.00pp**
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Last updated: 2026-04-11T12:43:21.933401+00:00
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version https://git-lfs.github.com/spec/v1
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oid sha256:353d1f8e9ebae3a1c9fe81c64b82a9919759b1303b36242f9318f1af2e5a94de
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size 205694
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- dataset:
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id: TIGER-Lab/MMLU-Pro
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task_id: mmlu_pro
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value: 75.0
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date: '2026-04-11'
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source:
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url: https://huggingface.co/lthn/lemmy/tree/main/.eval_results
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name: Canonical per-round parquet
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user: lthn
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notes: '8-PAC merged canon: 4 questions × 8 rounds = 32 samples across 1 machine(s)
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and 1 run(s). Google-calibrated sampling (temp=1.0, top_p=0.95, top_k=64), enable_thinking=True.
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Metric: majority-vote accuracy (headline).'
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- dataset:
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id: TIGER-Lab/MMLU-Pro
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task_id: mmlu_pro
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value: 71.88
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date: '2026-04-11'
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source:
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url: https://huggingface.co/lthn/lemmy/tree/main/.eval_results
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name: Canonical per-round parquet
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user: lthn
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notes: '8-PAC merged canon: 4 questions × 8 rounds = 32 samples across 1 machine(s)
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and 1 run(s). Google-calibrated sampling (temp=1.0, top_p=0.95, top_k=64), enable_thinking=True.
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Metric: per-round mean accuracy.'
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