Instructions to use NoQuest/LLmSave with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use NoQuest/LLmSave with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="NoQuest/LLmSave", filename="LLamandementFineTuneSansNotation16Q.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use NoQuest/LLmSave 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 NoQuest/LLmSave:Q8_0 # Run inference directly in the terminal: llama cli -hf NoQuest/LLmSave:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf NoQuest/LLmSave:Q8_0 # Run inference directly in the terminal: llama cli -hf NoQuest/LLmSave:Q8_0
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 NoQuest/LLmSave:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf NoQuest/LLmSave:Q8_0
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 NoQuest/LLmSave:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf NoQuest/LLmSave:Q8_0
Use Docker
docker model run hf.co/NoQuest/LLmSave:Q8_0
- LM Studio
- Jan
- Ollama
How to use NoQuest/LLmSave with Ollama:
ollama run hf.co/NoQuest/LLmSave:Q8_0
- Unsloth Studio
How to use NoQuest/LLmSave 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 NoQuest/LLmSave 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 NoQuest/LLmSave to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for NoQuest/LLmSave to start chatting
- Atomic Chat new
- Docker Model Runner
How to use NoQuest/LLmSave with Docker Model Runner:
docker model run hf.co/NoQuest/LLmSave:Q8_0
- Lemonade
How to use NoQuest/LLmSave with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NoQuest/LLmSave:Q8_0
Run and chat with the model
lemonade run user.LLmSave-Q8_0
List all available models
lemonade list
| from huggingface_hub import HfApi, HfFolder | |
| import os | |
| # Save your token | |
| HfFolder.save_token('...') | |
| api = HfApi() | |
| # Local path of the folder containing the model files | |
| local_folder_path = "./Dockerfiles" | |
| # Define the destination path in the repository | |
| repo_id = "NoQuest/LLmSave" | |
| repo_type = "model" | |
| print(f"Uploading {local_folder_path} to {repo_id}...") | |
| # Get a list of files and their sizes | |
| file_sizes = [] | |
| for root, _, files in os.walk(local_folder_path): | |
| for file in files: | |
| file_path = os.path.join(root, file) | |
| file_sizes.append((file_path, os.path.getsize(file_path))) | |
| # Sort files by size (smallest to largest) | |
| file_sizes.sort(key=lambda item: item[1]) | |
| # Upload files in order of size | |
| for file_path, size in file_sizes: | |
| print(f"Adding file: {file_path} (Size: {size} bytes)") | |
| api.upload_file( | |
| path_or_fileobj=file_path, | |
| path_in_repo=os.path.relpath(file_path, local_folder_path), | |
| repo_id=repo_id, | |
| repo_type=repo_type, | |
| ) | |
| print("Upload complete!") | |
| #user@r-noquest-qp-anmixtao-5pisb5tc-14d83-d7h2k:/data$ ls LLmSave/ | |
| #Dockerfiles README.md models on_startup.sh | |