Instructions to use SlyEcho/open_llama_3b_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use SlyEcho/open_llama_3b_gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SlyEcho/open_llama_3b_gguf", filename="open-llama-3b-f16.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 SlyEcho/open_llama_3b_gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SlyEcho/open_llama_3b_gguf:F16 # Run inference directly in the terminal: llama-cli -hf SlyEcho/open_llama_3b_gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SlyEcho/open_llama_3b_gguf:F16 # Run inference directly in the terminal: llama-cli -hf SlyEcho/open_llama_3b_gguf:F16
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 SlyEcho/open_llama_3b_gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf SlyEcho/open_llama_3b_gguf:F16
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 SlyEcho/open_llama_3b_gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf SlyEcho/open_llama_3b_gguf:F16
Use Docker
docker model run hf.co/SlyEcho/open_llama_3b_gguf:F16
- LM Studio
- Jan
- Ollama
How to use SlyEcho/open_llama_3b_gguf with Ollama:
ollama run hf.co/SlyEcho/open_llama_3b_gguf:F16
- Unsloth Studio new
How to use SlyEcho/open_llama_3b_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 SlyEcho/open_llama_3b_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 SlyEcho/open_llama_3b_gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SlyEcho/open_llama_3b_gguf to start chatting
- Docker Model Runner
How to use SlyEcho/open_llama_3b_gguf with Docker Model Runner:
docker model run hf.co/SlyEcho/open_llama_3b_gguf:F16
- Lemonade
How to use SlyEcho/open_llama_3b_gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SlyEcho/open_llama_3b_gguf:F16
Run and chat with the model
lemonade run user.open_llama_3b_gguf-F16
List all available models
lemonade list
gguf versions of OpenLLaMa 3B
- Version: 1T tokens final version
- Project: OpenLLaMA: An Open Reproduction of LLaMA
- Model: openlm-research/open_llama_3b
- llama.cpp: build 1012 (6381d4e) or later
- ggml version
Newer quantizations
There are now more quantization types in llama.cpp, some lower than 4 bits. Currently these are not supported, maybe because some weights have shapes that don't divide by 256.
Perplexity on wiki.test.406
Coming soon...
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Hardware compatibility
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