Instructions to use KnutJaegersberg/2-bit-LLMs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use KnutJaegersberg/2-bit-LLMs with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KnutJaegersberg/2-bit-LLMs", filename="WizardLM-70b-gguf-xs.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use KnutJaegersberg/2-bit-LLMs with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KnutJaegersberg/2-bit-LLMs # Run inference directly in the terminal: llama-cli -hf KnutJaegersberg/2-bit-LLMs
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KnutJaegersberg/2-bit-LLMs # Run inference directly in the terminal: llama-cli -hf KnutJaegersberg/2-bit-LLMs
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 KnutJaegersberg/2-bit-LLMs # Run inference directly in the terminal: ./llama-cli -hf KnutJaegersberg/2-bit-LLMs
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 KnutJaegersberg/2-bit-LLMs # Run inference directly in the terminal: ./build/bin/llama-cli -hf KnutJaegersberg/2-bit-LLMs
Use Docker
docker model run hf.co/KnutJaegersberg/2-bit-LLMs
- LM Studio
- Jan
- vLLM
How to use KnutJaegersberg/2-bit-LLMs with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KnutJaegersberg/2-bit-LLMs" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KnutJaegersberg/2-bit-LLMs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KnutJaegersberg/2-bit-LLMs
- Ollama
How to use KnutJaegersberg/2-bit-LLMs with Ollama:
ollama run hf.co/KnutJaegersberg/2-bit-LLMs
- Unsloth Studio new
How to use KnutJaegersberg/2-bit-LLMs 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 KnutJaegersberg/2-bit-LLMs 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 KnutJaegersberg/2-bit-LLMs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for KnutJaegersberg/2-bit-LLMs to start chatting
- Docker Model Runner
How to use KnutJaegersberg/2-bit-LLMs with Docker Model Runner:
docker model run hf.co/KnutJaegersberg/2-bit-LLMs
- Lemonade
How to use KnutJaegersberg/2-bit-LLMs with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KnutJaegersberg/2-bit-LLMs
Run and chat with the model
lemonade run user.2-bit-LLMs-{{QUANT_TAG}}List all available models
lemonade list
Ctrl+K
- 37.4 MB xet
- 23.6 MB xet
- 24.9 MB xet
- 27.1 MB xet
- 27.1 MB xet
- 3.57 MB xet
- 42.7 MB xet
- 24.9 MB xet
- 15.3 MB xet
- 24.9 MB xet
- 25.7 MB xet
- 4.99 MB xet
- 23.6 MB xet
- 24.9 MB xet
- 24.9 MB xet
- 43.6 MB xet
- 23.6 MB xet
- 25.7 MB xet
- 23.6 MB xet
- 23.6 MB xet
- 42.7 MB xet
- 24.9 MB xet
- 23.6 MB xet
- 15.3 MB xet
- 23.6 MB xet
- 24.9 MB xet
- 23.6 MB xet
- 15.3 MB xet
- 24.9 MB xet
- 24.9 MB xet