Instructions to use lthn/lemmy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

Snider Virgil commited on Apr 11

Commit

47852c7

1 Parent(s): 983d4b7

feat: add gguf multi-quant set (migrated from lthn/lemmy-gguf)

Browse files

Consolidating the lemmy gguf quants into this repo to match the lemer
pattern — everything for llama.cpp/Ollama users in one place, mlx
users redirected to sibling -mlx repos. After this lands the separate
lthn/lemmy-gguf repo will be deleted.

Files (5 quants, ~127GB total):
lemmy-bf16.gguf 47 GB full precision reference
lemmy-q8_0.gguf 25 GB near-lossless
lemmy-q6_k.gguf 21 GB
lemmy-q5_k_m.gguf 18 GB
lemmy-q4_k_m.gguf 16 GB recommended for Ollama

Xet should dedupe most of the upload against the existing blobs
HF already has in lthn/lemmy-gguf.

Co-Authored-By: Virgil <virgil@lethean.io>

Files changed (5) hide show

lemmy-bf16.gguf +3 -0
lemmy-q4_k_m.gguf +3 -0
lemmy-q5_k_m.gguf +3 -0
lemmy-q6_k.gguf +3 -0
lemmy-q8_0.gguf +3 -0

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