- Libraries
- Transformers
How to use mradermacher/LateOn-regularized-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="mradermacher/LateOn-regularized-GGUF") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/LateOn-regularized-GGUF", device_map="auto") - sentence-transformers
How to use mradermacher/LateOn-regularized-GGUF with sentence-transformers:
from pylate import models
queries = [
"Which planet is known as the Red Planet?",
"What is the largest planet in our solar system?",
]
documents = [
["Mars is the Red Planet.", "Venus is Earth's twin."],
["Jupiter is the largest planet.", "Saturn has rings."],
]
model = models.ColBERT(model_name_or_path="mradermacher/LateOn-regularized-GGUF")
queries_emb = model.encode(queries, is_query=True)
docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/LateOn-regularized-GGUF 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 mradermacher/LateOn-regularized-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/LateOn-regularized-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/LateOn-regularized-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/LateOn-regularized-GGUF: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 mradermacher/LateOn-regularized-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/LateOn-regularized-GGUF: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 mradermacher/LateOn-regularized-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/LateOn-regularized-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/LateOn-regularized-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/LateOn-regularized-GGUF with Ollama:
ollama run hf.co/mradermacher/LateOn-regularized-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/LateOn-regularized-GGUF 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 mradermacher/LateOn-regularized-GGUF 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 mradermacher/LateOn-regularized-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/LateOn-regularized-GGUF to start chatting
- Docker Model Runner
How to use mradermacher/LateOn-regularized-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/LateOn-regularized-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/LateOn-regularized-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/LateOn-regularized-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LateOn-regularized-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
