Image-Text-to-Text
GGUF
Safetensors
gemma4
llama.cpp
ollama
multimodal
Mixture of Experts
conversational
Instructions to use LetheanNetwork/lemmy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use LetheanNetwork/lemmy with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LetheanNetwork/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
- llama.cpp
How to use LetheanNetwork/lemmy with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LetheanNetwork/lemmy:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LetheanNetwork/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 LetheanNetwork/lemmy:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LetheanNetwork/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 LetheanNetwork/lemmy:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LetheanNetwork/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 LetheanNetwork/lemmy:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LetheanNetwork/lemmy:Q4_K_M
Use Docker
docker model run hf.co/LetheanNetwork/lemmy:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use LetheanNetwork/lemmy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LetheanNetwork/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": "LetheanNetwork/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/LetheanNetwork/lemmy:Q4_K_M
- Ollama
How to use LetheanNetwork/lemmy with Ollama:
ollama run hf.co/LetheanNetwork/lemmy:Q4_K_M
- Unsloth Studio new
How to use LetheanNetwork/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 LetheanNetwork/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 LetheanNetwork/lemmy to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LetheanNetwork/lemmy to start chatting
- Pi new
How to use LetheanNetwork/lemmy with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf LetheanNetwork/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": "LetheanNetwork/lemmy:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use LetheanNetwork/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 LetheanNetwork/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 LetheanNetwork/lemmy:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use LetheanNetwork/lemmy with Docker Model Runner:
docker model run hf.co/LetheanNetwork/lemmy:Q4_K_M
- Lemonade
How to use LetheanNetwork/lemmy with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LetheanNetwork/lemmy:Q4_K_M
Run and chat with the model
lemonade run user.lemmy-Q4_K_M
List all available models
lemonade list
| { | |
| "audio_token": "<|audio|>", | |
| "backend": "tokenizers", | |
| "boa_token": "<|audio>", | |
| "boi_token": "<|image>", | |
| "bos_token": "<bos>", | |
| "eoa_token": "<audio|>", | |
| "eoc_token": "<channel|>", | |
| "eoi_token": "<image|>", | |
| "eos_token": "<eos>", | |
| "eot_token": "<turn|>", | |
| "escape_token": "<|\"|>", | |
| "etc_token": "<tool_call|>", | |
| "etd_token": "<tool|>", | |
| "etr_token": "<tool_response|>", | |
| "extra_special_tokens": [ | |
| "<|video|>" | |
| ], | |
| "image_token": "<|image|>", | |
| "mask_token": "<mask>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "padding_side": "left", | |
| "processor_class": "Gemma4Processor", | |
| "response_schema": { | |
| "type": "object", | |
| "properties": { | |
| "role": { | |
| "const": "assistant" | |
| }, | |
| "thinking": { | |
| "type": "string" | |
| }, | |
| "content": { | |
| "type": "string" | |
| }, | |
| "tool_calls": { | |
| "x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>", | |
| "type": "array", | |
| "items": { | |
| "type": "object", | |
| "properties": { | |
| "type": { | |
| "const": "function" | |
| }, | |
| "function": { | |
| "type": "object", | |
| "x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})", | |
| "properties": { | |
| "name": { | |
| "type": "string" | |
| }, | |
| "arguments": { | |
| "type": "object", | |
| "x-parser": "gemma4-tool-call", | |
| "additionalProperties": {} | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<content>(?:(?!\\<\\|tool_call\\>)(?!\\<turn\\|\\>).)+)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?:\\<turn\\|\\>)?" | |
| }, | |
| "soc_token": "<|channel>", | |
| "sot_token": "<|turn>", | |
| "stc_token": "<|tool_call>", | |
| "std_token": "<|tool>", | |
| "str_token": "<|tool_response>", | |
| "think_token": "<|think|>", | |
| "tokenizer_class": "GemmaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |